Current Bioinformatics Tools in Precision Oncology

Abstract

Integrating bioinformatics tools has profoundly transformed precision oncology by identifying essential molecular targets for personalized treatment. The rapid development of high-throughput sequencing and multiomics technologies creates complex datasets that require robust computational methods to extract meaningful insights. Nonetheless, the clinical application of multiomics data continues to pose significant challenges. This review explores advanced bioinformatics tools utilized within multiomics, emphasizing their pivotal role in discovering cancer biomarkers. Cloud-based platforms, such as Galaxy and DNAnexus, facilitate streamlined data processing, while single-cell analysis software, including Seurat, identifies rare cellular subpopulations. Further integration of artificial intelligence with machine learning approaches improves predictive modeling and diagnostic accuracy. Spatial omics technologies correlate molecular signatures within tumor microenvironments, guiding treatment strategies. Bioinformatics integrates these technologies to establish a new standard in precision oncology, thereby enhancing therapy efficacy. Collaborative initiatives between The Cancer Genome Atlas and cBioPortal expedite advancements through the sharing open data and implementing standardized methodologies. Advancing multiomics integration techniques alongside improved computational capabilities is essential for discovering new biomarkers and refining precision medicine strategies. Future efforts should focus on merging multiomics techniques with innovative computational methods to drive novel biomarker discovery and improve precision medicine applications.

Keywords

bioinformatics / biomarkers / multiomic / oncotherapeutics / precision oncology

References

[1] G. Molla, M. Bitew, “Revolutionizing Personalized Medicine: Synergy With Multi-Omics Data Generation, Main Hurdles, and Future Perspectives,” Biomedicines 12, no. 12 (2024): 2750.

[2] R. C. Wang, Z. Wang, “Precision Medicine: Disease Subtyping and Tailored Treatment,” Cancers 15, no. 15 (2023): 3837.

[3] R. Vitorino, “Transforming Clinical Research: The Power of High-Throughput Omics Integration,” Proteomes 12, no. 3 (2024): 25.

[4] P. Krzyszczyk, A. Acevedo, E. J. Davidoff, et al., “The Growing Role of Precision and Personalized Medicine for Cancer Treatment,” Technology (Singap World Science) 6, no. 3-4 (2018): 79-100.

[5] A. Passaro, M. Al Bakir, E. G. Hamilton, “Cancer Biomarkers—Emerging Trends and Clinical Implications for Personalized Treatment,” Cell 187, no. 7 (2024): 1617-1635.

[6] H. Xu, Z. Jia, F. Liu, et al., “Biomarkers and Experimental Models for Cancer Immunology Investigation,” MedComm 4, no. 6 (2023): e437.

[7] Y. Zhou, L. Tao, J. Qiu, et al., “Tumor Biomarkers for Diagnosis, Prognosis and Targeted Therapy,” Signal Transduction and Targeted Therapy 9, no. 1 (2024): 132.

[8] Unravelling the Role of Biomarker in Cancer Detection: An In-Depth Review | Current Pharmacology Reports. Accessed April 29, 2025. https://link.springer.com/article/10.1007/s40495-025-00413-2

[9] R. U. Quraish, T. Hirahata, A. U. Quraish, “Ul Quraish S. An Overview: Genetic Tumor Markers for Early Detection and Current Gene Therapy Strategies,” Cancer Inform 22 (2023): 11769351221150772.

[10] C. Sha, P. C. Lee, “EGFR-Targeted Therapies: A Literature Review,” Journal of Clinical Medicine 13, no. 21 (2024): 6391.

[11] K. Kamińska, E. Nalejska, M. Kubiak, et al., “Prognostic and Predictive Epigenetic Biomarkers in Oncology,” Molecular Diagnosis & Therapy 23, no. 1 (2019): 83-95.

[12] T. Wolde, J. Huang, P. Huang, V. Pandey, P. Qin, “Depleted-MLH1 Expression Predicts Prognosis and Immunotherapeutic Efficacy in Uterine Corpus Endometrial Cancer: An in Silico Approach,” BioMedInformatics 4, no. 1 (2024): 326-346.

[13] Y. Zhang, J. Wu, C. Zhao, S. Zhang, J. Zhu, “Recent Advancement of PD-L1 Detection Technologies and Clinical Applications in the Era of Precision Cancer Therapy,” Journal of Cancer 14, no. 5 (2023): 850-873.

[14] A. Ragupathi, M. Singh, A. M. Perez, D. Zhang, “Targeting the BRCA1/2 Deficient Cancer With PARP Inhibitors: Clinical Outcomes and Mechanistic Insights,” Frontiers in Cell and Developmental Biology 11 (2023): 1133472.

[15] A. M. Bode, Z. Dong, “Recent Advances in Precision Oncology Research,” Npj Precision Oncology 2, no. 1 (2018): 1-6.

[16] A. J. Clark, J. W. Lillard, “A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology,” Genes (Basel) 15, no. 8 (2024): 1036.

[17] S. Dotolo, R. Esposito Abate, C. Roma, “Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice,” Biomedicines 10, no. 9 (2022): 2074.

[18] T. Wolde, V. Bhardwaj, M. Reyad-Ul-Ferdous, P. Qin, V. Pandey, “The Integrated Bioinformatic Approach Reveals the Prognostic Significance of LRP1 Expression in Ovarian Cancer,” International Journal of Molecular Sciences 25, no. 14 (2024): 7996.

[19] H. Zhao, X. Zhang, L. Guo, S. Shi, C. Lu, “A Robust Seven-Gene Signature Associated with Tumor Microenvironment to Predict Survival Outcomes of Patients with Stage III-IV Lung Adenocarcinoma,” Frontiers in Genetics 12 (2021): 684281.

[20] V. P. Snijesh, S. Krishnamurthy, V. Bhardwaj, et al., “SHH Signaling as a Key Player in Endometrial Cancer: Unveiling the Correlation With Good Prognosis, Low Proliferation, and Anti-Tumor Immune Milieu,” International Journal of Molecular Sciences 25, no. 19 (2024): 10443.

[21] D. Rosati, M. Palmieri, G. Brunelli, et al., “Differential Gene Expression Analysis Pipelines and Bioinformatic Tools for the Identification of Specific Biomarkers: A Review,” Computational and Structural Biotechnology Journal 23 (2024): 1154-1168.

[22] M. Adnane, A. M. de Almeida, A. Chapwanya, “Unveiling the Power of Proteomics in Advancing Tropical Animal Health and Production,” Tropical Animal Health and Production 56, no. 5 (2024): 182.

[23] C. Chen, J. Wang, D. Pan, et al., “Applications of Multi-omics Analysis in human Diseases,” MedComm 4, no. 4 (2023): e315.

[24] I. Subramanian, S. Verma, S. Kumar, A. Jere, K. Anamika, “Multi-omics Data Integration, Interpretation, and Its Application,” Bioinform Biol Insights 14 (2020): 1177932219899051.

[25] C. J. Cremin, S. Dash, X. Huang, “Big Data: Historic Advances and Emerging Trends in Biomedical Research,” Current Research in Biotechnology 4 (2022): 138-151.

[26] R. J. Woodman, A. A. Mangoni, “A Comprehensive Review of Machine Learning Algorithms and Their Application in Geriatric Medicine: Present and Future,” Aging Clinical and Experimental Research 35, no. 11 (2023): 2363-2397.

[27] H. Shimizu, K. I. Nakayama, “Artificial Intelligence in Oncology,” Cancer Science 111, no. 5 (2020): 1452-1460.

[28] M. V. C. Santos, A. S. Feltrin, I. C. Costa-Amaral, et al., “Network Analysis of Biomarkers Associated With Occupational Exposure to Benzene and Malathion,” International Journal of Molecular Sciences 24, no. 11 (2023): 9415.

[29] R. Asiimwe, S. Lam, Leung, et al., “From Biobank and Data Silos Into a Data Commons: Convergence to Support Translational Medicine,” Journal of Translational Medicine 19 (2021): 493.

[30] V. Brancato, G. Esposito, L. Coppola, et al., “Standardizing Digital Biobanks: Integrating Imaging, Genomic, and Clinical Data for Precision Medicine,” Journal of Translational Medicine 22, no. 1 (2024): 136.

[31] H. Satam, K. Joshi, U. Mangrolia, et al., “Next-Generation Sequencing Technology: Current Trends and Advancements,” Biology (Basel) 12, no. 7 (2023): 997.

[32] E. Papadaki, I. Kakkos, P. Vlamos, et al., “Recent Web Platforms for Multi-Omics Integration Unlocking Biological Complexity,” Applied Sciences 15, no. 1 (2025): 329.

[33] A. Jose, P. Kulkarni, J. Thilakan, et al., “Integration of Pan-omics Technologies and Three-dimensional in Vitro Tumor Models: An Approach Toward Drug Discovery and Precision Medicine,” Molecular Cancer 23, no. 1 (2024): 50.

[34] Y. Qin, Y. Liu, X. Xiang, et al., “Cuproptosis Correlates With Immunosuppressive Tumor Microenvironment Based on Pan-cancer Multiomics and Single-cell Sequencing Analysis,” Molecular Cancer 22, no. 1 (2023): 59.

[35] F. Menghi, E. T. Liu, “Functional Genomics of Complex Cancer Genomes,” Nature Communications 13, no. 1 (2022): 5908.

[36] S. E. Levy, B. E. Boone, “Next-Generation Sequencing Strategies,” Cold Spring Harbor Perspectives in Medicine 9, no. 7 (2019): a025791.

[37] Y. Tang, Y. Fan, “Combined KRAS and TP53 Mutation in Patients With Colorectal Cancer Enhance Chemoresistance to Promote Postoperative Recurrence and Metastasis,” BMC Cancer 24 (2024): 1155.

[38] C. Perfetto, M. Aprile, S. Cataldi, E. Giovannetti, V. Costa, “Unraveling BRAF Alterations: Molecular Insights to Circumvent Therapeutic Resistance Across Cancer Types,” Cancer Drug Resistance 8 (2025): 14.

[39] A. McKenna, M. Hanna, E. Banks, et al., “The Genome Analysis Toolkit: A MapReduce Framework for Analyzing next-generation DNA Sequencing Data,” Genome Research 20, no. 9 (2010): 1297-1303.

[40] K. Cibulskis, M. S. Lawrence, S. L. Carter, et al., “Sensitive Detection of Somatic Point Mutations in Impure and Heterogeneous Cancer Samples,” Nature Biotechnology 31, no. 3 (2013): 213-219.

[41] K. Wang, M. Li, H. Hakonarson, “ANNOVAR: Functional Annotation of Genetic Variants From High-throughput Sequencing Data,” Nucleic Acids Research 38, no. 16 (2010): e164.

[42] M. Arigoni, M. L. Ratto, F. Riccardo, et al., “A Single Cell RNAseq Benchmark Experiment Embedding “Controlled” Cancer Heterogeneity,” Scientific Data 11, no. 1 (2024): 159.

[43] D. Sun, M. Liu, F. Huang, F. Huang, “[Bioinformatics analysis of expression and function of EXD3 gene in gastric cancer],” Nan Fang Yi Ke Da Xue Xue Bao 39, no. 2 (2019): 215-221.

[44] K. Yan, Q. Z. Liu, R. R. Huang, et al., “Spatial Transcriptomics Reveals Prognosis-associated Cellular Heterogeneity in the Papillary Thyroid Carcinoma Microenvironment,” Clinical and Translational Medicine 14, no. 3 (2024): e1594.

[45] Y. Ma, X. Zhou, “Spatially Informed Cell Type Deconvolution for Spatial Transcriptomics,” Nature Biotechnology 40, no. 9 (2022): 1349-1359. Published online May 2, 2022.

[46] S. Zhao, W. P. Fung-Leung, A. Bittner, K. Ngo, X. Liu, “Comparison of RNA-Seq and Microarray in Transcriptome Profiling of Activated T Cells,” PLoS ONE 9, no. 1 (2014): e78644.

[47] A. Mortazavi, B. A. Williams, K. McCue, L. Schaeffer, B. Wold, “Mapping and Quantifying Mammalian Transcriptomes by RNA-Seq,” Nature Methods 5, no. 7 (2008): 621-628.

[48] B. Zhao, B. Li, Y. Chen, et al., “CDKN2A is a Promising Diagnostic and Prognostic Biomarker and Associations With Immune Infiltrates in Colorectal Cancer,” Heliyon Published online March 6, 2025:e43049, https://doi.org/10.1016/j.heliyon.2025.e43049.

[49] H. El Ahanidi, M. El Azzouzi, B. Addoum, “STAT1 and STAT4 Expression as Prognostic Biomarkers in Patients With Bladder Cancer,” Molecular and Clinical Oncology 22, no. 4 (2025): 1-7.

[50] S. Zhang, K. Liu, Y. Liu, X. Hu, X. Gu, “The Role and Application of Bioinformatics Techniques and Tools in Drug Discovery,” Frontiers in Pharmacology 16 (2025): 1547131.

[51] A. Siavoshi, M. Taghizadeh, E. Dookhe, M. Piran, “Gene Expression Profiles and Pathway Enrichment Analysis to Identification of Differentially Expressed Gene and Signaling Pathways in Epithelial Ovarian Cancer Based on High-throughput RNA-seq Data,” Genomics 114, no. 1 (2022): 161-170.

[52] F. Finotello, E. Lavezzo, L. Bianco, et al., “Reducing Bias in RNA Sequencing Data: A Novel Approach to Compute Counts,” BMC Bioinformatics [Electronic Resource] 15, no. Suppl 1 (2014): S7.

[53] D. Chen, L. Xu, H. Xing, et al., “Sangerbox 2: Enhanced Functionalities and Update for a Comprehensive Clinical Bioinformatics Data Analysis Platform,” Imeta 3, no. 5 (2024): e238.

[54] F. Rohart, B. Gautier, A. Singh, K. A. Lê Cao, “mixOmics: An R Package for 'Omics Feature Selection and Multiple Data Integration,” Plos Computational Biology 13, no. 11 (2017): e1005752.

[55] R. Argelaguet, B. Velten, D. Arnol, et al., “Multi-Omics Factor Analysis-a Framework for Unsupervised Integration of Multi-omics Data Sets,” Molecular Systems Biology 14, no. 6 (2018): e8124.

[56] T. Stuart, A. Butler, P. Hoffman, et al., “Comprehensive Integration of Single-Cell Data,” Cell 177, no. 7 (2019): 1888-1902. e21.

[57] F. A. Wolf, P. Angerer, F. J. Theis, “SCANPY: Large-scale Single-cell Gene Expression Data Analysis,” Genome Biology 19 (2018): 15.

[58] Y. Cui, S. Zhang, Y. Liang, X. Wang, T. N. Ferraro, Y. Chen, “Consensus Clustering of Single-cell RNA-seq Data by Enhancing Network Affinity,” Briefings in Bioinformatics 22, no. 6 (2021): bbab236.

[59] M. Valerio, A. Inno, S. Gori, “pyBioPortal: A Python Package for Simplifying cBioPortal Data Access in Cancer Research,” JAMIA Open 8, no. 1 (2025): ooae146.

[60] P. Brlek, A. Kafka, A. Bukovac, “Pećina-Šlaus N. Integrative cBioPortal Analysis Revealed Molecular Mechanisms That Regulate EGFR-PI3K-AKT-mTOR Pathway in Diffuse Gliomas of the Brain,” Cancers 13, no. 13 (2021): 3247.

[61] Patro R., G. Duggal, M. I. Love, R. A. Irizarry, C. Kingsford, “Salmon Provides Fast and Bias-aware Quantification of Transcript Expression,” Nature Methods 14, no. 4 (2017): 417-419.

[62] C. Robert, M. Watson, “Errors in RNA-Seq Quantification Affect Genes of Relevance to human Disease,” Genome Biology 16, no. 1 (2015): 177.

[63] S. Anders, P. T. Pyl, W. Huber, “HTSeq-a Python Framework to Work With High-throughput Sequencing Data,” Bioinformatics 31, no. 2 (2015): 166-169.

[64] L. A. Corchete, E. A. Rojas, D. Alonso-López, et al., “Systematic Comparison and Assessment of RNA-seq Procedures for Gene Expression Quantitative Analysis,” Scientific Reports 10, no. 1 (2020): 19737.

[65] S. J. Fleming, M. D. Chaffin, A. Arduini, et al., “Unsupervised Removal of Systematic Background Noise From Droplet-based Single-cell Experiments Using CellBender,” Nature Methods 20, no. 9 (2023): 1323-1335.

[66] J. Ding, A. Regev, “Deep Generative Model Embedding of Single-cell RNA-Seq Profiles on Hyperspheres and Hyperbolic Spaces,” Nature Communications 12, no. 1 (2021): 2554.

[67] R. Cannoodt, W. Saelens, L. Deconinck, Y. Saeys, “Spearheading Future Omics Analyses Using dyngen, a Multi-modal Simulator of Single Cells,” Nature Communications 12, no. 1 (2021): 3942.

[68] R. Cannoodt, W. Saelens, L. Deconinck, Y. Saeys. dyngen: a multi-modal simulator for spearheading new single-cell omics analyses. Published online September 14, 2020:2020.02.06.936971. https://doi.org/10.1101/2020.02.06.936971

[69] Y. Cao, J. Zhu, P. Jia, Z. Zhao, “scRNASeqDB: A Database for RNA-Seq Based Gene Expression Profiles in Human Single Cells,” Genes (Basel) 8, no. 12 (2017): 368.

[70] X. Cui, F. Qin, X. Yu, F. Xiao, G. Cai, “SCISSOR^TM: A Single-cell Inferred Site-specific Omics Resource for Tumor Microenvironment Association Study,” NAR Cancer 3, no. 3 (2021): zcab037.

[71] M. Su, Z. Zhang, L. Zhou, C. Han, C. Huang, E. C. Nice, “Proteomics, Personalized Medicine and Cancer,” Cancers 13, no. 11 (2021): 2512.

[72] Z. Zhou, R. Zhang, A. Zhou, et al., “Proteomics Appending a Complementary Dimension to Precision Oncotherapy,” Computational and Structural Biotechnology Journal 23 (2024): 1725-1739.

[73] D. M. Berney, “Biomarkers for Prostate Cancer Detection and Progression: Beyond Prostate-specific Antigen,” Drug News & Perspectives 23, no. 3 (2010): 185-194.

[74] G. J. S. Rustin, M. E. L. van der Burg, C. L. Griffin, et al., “Early versus Delayed Treatment of Relapsed Ovarian Cancer (MRC OV05/EORTC 55955): A Randomised Trial,” Lancet 376, no. 9747 (2010): 1155-1163.

[75] S. Wu, S. Zhang, C. M. Liu, A. R. Fernie, S. Yan, “Recent Advances in Mass Spectrometry-Based Protein Interactome Studies,” Molecular & Cellular Proteomics 24, no. 1 (2025): 100887.

[76] J. Li, H. J. Zhu, “Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS)-Based Proteomics of Drug-Metabolizing Enzymes and Transporters,” Molecules (Basel, Switzerland) 25, no. 11 (2020): 2718.

[77] O. Stoevesandt, M. J. Taussig, M. He, “Protein Microarrays: High-throughput Tools for Proteomics,” Expert Review of Proteomics 6, no. 2 (2014): 145-157.

[78] A. Palomba, M. Abbondio, G. Fiorito, S. Uzzau, D. Pagnozzi, A. Tanca, “Comparative Evaluation of MaxQuant and Proteome Discoverer MS1-Based Protein Quantification Tools,” Journal of Proteome Research 20, no. 7 (2021): 3497-3507.

[79] B. MacLean, D. M. Tomazela, N. Shulman, et al., “Skyline: An Open Source Document Editor for Creating and Analyzing Targeted Proteomics Experiments,” Bioinformatics 26, no. 7 (2010): 966-968.

[80] B. C. Orsburn, “Proteome Discoverer-A Community Enhanced Data Processing Suite for Protein Informatics,” Proteomes 9, no. 1 (2021): 15.

[81] Y. M. Farag, C. Horro, M. Vaudel, H. Barsnes, “PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data,” Journal of Proteome Research 20, no. 12 (2021): 5419-5423.

[82] T. D. Müller, A. Siraj, A. Walter, et al., “OpenMS WebApps: Building User-Friendly Solutions for MS Analysis,” Journal of Proteome Research 24, no. 2 (2025): 940-948. Published online January 30, 2025.

[83] OpenMS. Accessed April 30, 2025. https://openms.de/

[84] D. Szklarczyk, A. L. Gable, D. Lyon, et al., “STRING v11: Protein-protein Association Networks With Increased Coverage, Supporting Functional Discovery in Genome-wide Experimental Datasets,” Nucleic Acids Research 47, no. Database issue (2019): D607-D613.

[85] N. T. Doncheva, J. H. Morris, J. Gorodkin, L. J. Jensen, “Cytoscape StringApp: Network Analysis and Visualization of Proteomics Data,” Journal of Proteome Research 18, no. 2 (2019): 623-632.

[86] R. Oughtred, C. Stark, B. J. Breitkreutz, et al., “The BioGRID Interaction Database: 2019 Update,” Nucleic Acids Research 47, no. D1 (2019): D529-D541.

[87] P. V. Hornbeck, J. M. Kornhauser, S. Tkachev, et al., “PhosphoSitePlus: A Comprehensive Resource for Investigating the Structure and Function of Experimentally Determined Post-translational Modifications in man and Mouse,” Nucleic Acids Research 40, no. Database issue (2012): D261-270.

[88] S. Rosignoli, M. Pacelli, F. Manganiello, A. Paiardini, “An Outlook on Structural Biology After AlphaFold: Tools, Limits and Perspectives,” FEBS Open Bio 15, no. 2 (2025): 202-222.

[89] J. Yang, Y. Zhang, “Protein Structure and Function Prediction Using I-TASSER,” CP in Bioinformatics 52 (2015): 5.8.1-5.815.

[90] Y. Chen, H. Zhang, W. Wang, Y. Shen, Z. Ping, “Rapid Generation of High-quality Structure Figures for Publication With PyMOL-PUB,” Bioinformatics 40, no. 3 (2024): btae139.

[91] H. Takeda, Y. Matsuzawa, M. Takeuchi, et al., “MS-DIAL 5 Multimodal Mass Spectrometry Data Mining Unveils Lipidome Complexities,” Nature Communications 15, no. 1 (2024): 9903.

[92] V. Demichev, L. Szyrwiel, F. Yu, et al., “dia-PASEF Data Analysis Using FragPipe and DIA-NN for Deep Proteomics of Low Sample Amounts,” Nature Communications 13, no. 1 (2022): 3944.

[93] N. H. Tran, X. Zhang, L. Xin, B. Shan, M. Li, “De Novo Peptide Sequencing by Deep Learning,” Proceedings of the National Academy of Sciences 114, no. 31 (2017): 8247-8252.

[94] S. H. Yu, D. Ferretti, J. P. Schessner, J. D. Rudolph, G. H. H. Borner, J. Cox, “Expanding the Perseus Software for Omics Data Analysis with Custom Plugins,” Current Protocols in Bioinformatics 71, no. 1 (2020): e105.

[95] Y. Chen, L. Chen, A. T. L. Lun, P. L. Baldoni, G. K. Smyth, “edgeR v4: Powerful Differential Analysis of Sequencing Data With Expanded Functionality and Improved Support for Small Counts and Larger Datasets,” Nucleic Acids Research 53, no. 2 (2025): gkaf018.

[96] M. Gu, B. Ren, Y. Fang, et al., “Epigenetic Regulation in Cancer,” MedComm 5, no. 2 (2024): e495.

[97] K. Struhl, “The Distinction Between Epigenetics and Epigenomics,” Trends in Genetics 40, no. 12 (2024): 995-997.

[98] A. P. Feinberg, M. A. Koldobskiy, A. Göndör, “Epigenetic Modulators, Modifiers and Mediators in Cancer Aetiology and Progression,” Nature Reviews Genetics 17, no. 5 (2016): 284-299.

[99] W. Zhang, L. Qi, Z. Liu, et al., “Integrated Multiomic Analysis and High-throughput Screening Reveal Potential Gene Targets and Synergetic Drug Combinations for Osteosarcoma Therapy,” MedComm 4, no. 4 (2023): e317.

[100] X. Chen, H. Xu, X. Shu, C. X. Song, “Mapping Epigenetic Modifications by Sequencing Technologies,” Cell Death and Differentiation 32, no. 1 (2025): 56-65.

[101] M. Kernaleguen, C. Daviaud, Y. Shen, et al., “Whole-Genome Bisulfite Sequencing for the Analysis of Genome-Wide DNA Methylation and Hydroxymethylation Patterns at Single-Nucleotide Resolution,” Methods in Molecular Biology 1767 (2018): 311-349.

[102] C. C. Pritchard, H. H. Cheng, M. Tewari, “MicroRNA Profiling: Approaches and Considerations,” Nature Reviews Genetics 13, no. 5 (2012): 358-369.

[103] T. S. Furey, “ChIP-seq and Beyond: New and Improved Methodologies to Detect and Characterize Protein-DNA Interactions,” Nature Reviews Genetics 13, no. 12 (2012): 840-852.

[104] R. Prabhakaran, R. Thamarai, S. Sivasamy, et al., “Epigenetic Frontiers: MiRNAs, Long Non-coding RNAs and Nanomaterials Are Pioneering to Cancer Therapy,” Epigenetics & Chromatin 17, no. 1 (2024): 31.

[105] V. Bhardwaj, Y. Q. Tan, M. M. Wu, et al., “Long Non-coding RNAs in Recurrent Ovarian Cancer: Theranostic Perspectives,” Cancer Letters 502 (2021): 97-107.

[106] M. M. Kadhim, A. A. Ramírez-Coronel, A. T. Jalil, et al., “Autophagy as a Self-digestion Signal in human Cancers: Regulation by microRNAs in Affecting Carcinogenesis and Therapy Response,” Pharmacological Research 189 (2023): 106695.

[107] S. Kansara, V. Pandey, P. E. Lobie, G. Sethi, M. Garg, A. K. Pandey, “Mechanistic Involvement of Long Non-Coding RNAs in Oncotherapeutics Resistance in Triple-Negative Breast Cancer,” Cells 9, no. 6 (2020): 1511.

[108] M. A. Zandieh, M. H. Farahani, R. Rajabi, “Epigenetic Regulation of Autophagy by Non-coding RNAs in Gastrointestinal Tumors: Biological Functions and Therapeutic Perspectives,” Pharmacological Research 187 (2023): 106582.

[109] M. Garg, G. Sethi, “Emerging Role of Long Non-coding RNA (lncRNA) in human Malignancies: A Unique Opportunity for Precision Medicine,” Cancer Letters 519 (2021): 1.

[110] R. Elango, V. Radhakrishnan, S. Rashid, et al., “Long Noncoding RNA Profiling Unveils LINC00960 as Unfavorable Prognostic Biomarker Promoting Triple Negative Breast Cancer Progression,” Cell Death Discovery 10, no. 1 (2024): 333.

[111] F. Krueger, S. R. Andrews, “Bismark: A Flexible Aligner and Methylation Caller for Bisulfite-Seq Applications,” Bioinformatics 27, no. 11 (2011): 1571-1572.

[112] A. Akalin, M. Kormaksson, S. Li, et al., “methylKit: A Comprehensive R Package for the Analysis of Genome-wide DNA Methylation Profiles,” Genome Biology 13, no. 10 (2012): R87.

[113] H. Feng, K. N. Conneely, H. Wu, “A Bayesian Hierarchical Model to Detect Differentially Methylated Loci From Single Nucleotide Resolution Sequencing Data,” Nucleic Acids Research 42, no. 8 (2014): e69.

[114] Y. Zhang, T. Liu, C. A. Meyer, et al., “Model-based Analysis of ChIP-Seq (MACS),” Genome Biology 9, no. 9 (2008): R137.

[115] C. Zang, D. E. Schones, C. Zeng, K. Cui, K. Zhao, W. Peng, “A Clustering Approach for Identification of Enriched Domains From Histone Modification ChIP-Seq Data,” Bioinformatics 25, no. 15 (2009): 1952-1958.

[116] J. Ernst, M. Kellis, “ChromHMM: Automating Chromatin-state Discovery and Characterization,” Nature Methods 9, no. 3 (2012): 215-216.

[117] L. Shen, N. Y. Shao, X. Liu, I. Maze, J. Feng, E. J. Nestler, “diffReps: Detecting Differential Chromatin Modification Sites From ChIP-seq Data With Biological Replicates,” PLoS ONE 8, no. 6 (2013): e65598.

[118] M. E. Ritchie, B. Phipson, D. Wu, et al., “limma Powers Differential Expression Analyses for RNA-sequencing and Microarray Studies,” Nucleic Acids Research 43, no. 7 (2015): e47.

[119] M. D. Robinson, D. J. McCarthy, G. K. Smyth, “edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data,” Bioinformatics 26, no. 1 (2010): 139-140.

[120] F. Sarno, G. Benincasa, M. List, et al., “Clinical Epigenetics Settings for Cancer and Cardiovascular Diseases: Real-life Applications of Network Medicine at the Bedside,” Clinical Epigenetics 13, no. 1 (2021): 66.

[121] M. Friedemann, F. Horn, K. Gutewort, et al., “Increased Sensitivity of Detection of RASSF1A and GSTP1 DNA Fragments in Serum of Prostate Cancer Patients: Optimisation of Diagnostics Using OBBPA-ddPCR,” Cancers (Basel) 13, no. 17 (2021): 4459.

[122] M. K. Skinner, “Epigenetic Biomarkers for Disease Susceptibility and Preventative Medicine,” Cell Metabolism 36, no. 2 (2024): 263-277.

[123] X. Li, Y. Yang, B. Zhang, et al., “Lactate Metabolism in human Health and Disease,” Signal Transduction and Targeted Therapy 7, no. 1 (2022): 305.

[124] S. Qiu, Y. Cai, H. Yao, et al., “Small Molecule Metabolites: Discovery of Biomarkers and Therapeutic Targets,” Signal Transduction and Targeted Therapy 8, no. 1 (2023): 132.

[125] D. R. Schmidt, R. Patel, D. G. Kirsch, C. A. Lewis, M. G. Vander Heiden, J. W. Locasale, “Metabolomics in Cancer Research and Emerging Applications in Clinical Oncology,” CA: A Cancer Journal for Clinicians 71, no. 4 (2021): 333-358.

[126] L. Dang, D. W. White, S. Gross, et al., “Cancer-associated IDH1 Mutations Produce 2-hydroxyglutarate,” Nature 462, no. 7274 (2009): 739-744.

[127] J. H. Wang, W. L. Chen, J. M. Li, et al., “Prognostic Significance of 2-hydroxyglutarate Levels in Acute Myeloid Leukemia in China,” Proceedings of the National Academy of Sciences 110, no. 42 (2013): 17017-17022.

[128] S. Pan, L. Yin, J. Liu, et al., “Metabolomics-driven Approaches for Identifying Therapeutic Targets in Drug Discovery,” MedComm 5, no. 11 (2024): e792.

[129] W. Fu, A. Sun, H. Dai, “Lipid Metabolism Involved in Progression and Drug Resistance of Breast Cancer,” Genes & Diseases 12, no. 4 (2024): 101376.

[130] W. Wang, Z. Rong, G. Wang, Y. Hou, F. Yang, M. Qiu, “Cancer Metabolites: Promising Biomarkers for Cancer Liquid Biopsy,” Biomarker Research 11, no. 1 (2023): 66.

[131] F. Bhinderwala, N. Wase, C. DiRusso, R. Powers, “Combining Mass Spectrometry and NMR Improves Metabolite Detection and Annotation,” Journal of Proteome Research 17, no. 11 (2018): 4017-4022.

[132] A. H. Emwas, R. Roy, R. T. McKay, et al., “NMR Spectroscopy for Metabolomics Research,” Metabolites 9, no. 7 (2019): 123.

[133] A. H. M. Emwas, Z. A. Al-Talla, Y. Yang, N. M. Kharbatia, “Gas Chromatography-mass Spectrometry of Biofluids and Extracts,” Methods in Molecular Biology 1277 (2015): 91-112.

[134] M. Ciborowski, A. Lipska, J. Godzien, et al., “Combination of LC-MS- and GC-MS-based Metabolomics to Study the Effect of Ozonated Autohemotherapy on human Blood,” Journal of Proteome Research 11, no. 12 (2012): 6231-6241.

[135] H. P. Benton, D. M. Wong, S. A. Trauger, G. Siuzdak, “XCMS2: Processing Tandem Mass Spectrometry Data for Metabolite Identification and Structural Characterization,” Analytical Chemistry 80, no. 16 (2008): 6382.

[136] W. Niu, E. Knight, Q. Xia, B. D. McGarvey, “Comparative Evaluation of Eight Software Programs for Alignment of Gas Chromatography-mass Spectrometry Chromatograms in Metabolomics Experiments,” Journal of Chromatography A 1374 (2014): 199-206.

[137] Z. Pang, J. Chong, G. Zhou, et al., “MetaboAnalyst 5.0: Narrowing the Gap Between Raw Spectra and Functional Insights,” Nucleic Acids Research 49, no. W1 (2021): W388-W396.

[138] Z. Pang, Y. Lu, G. Zhou, et al., “MetaboAnalyst 6.0: Towards a Unified Platform for Metabolomics Data Processing, Analysis and Interpretation,” Nucleic Acids Research 52, no. W1 (2024): W398-W406.

[139] C. Ruttkies, E. L. Schymanski, S. Wolf, J. Hollender, S. Neumann, “MetFrag Relaunched: Incorporating Strategies Beyond in Silico Fragmentation,” Journal of Cheminformatics 8 (2016): 3.

[140] L. F. Nothias, D. Petras, R. Schmid, et al., “Feature-Based Molecular Networking in the GNPS Analysis Environment,” Nature Methods 17, no. 9 (2020): 905-908.

[141] B. Zhang, S. Hu, E. Baskin, A. Patt, J. K. Siddiqui, E. A. Mathé, “RaMP: A Comprehensive Relational Database of Metabolomics Pathways for Pathway Enrichment Analysis of Genes and Metabolites,” Metabolites 8, no. 1 (2018): 16.

[142] D. S. Wishart, A. Guo, E. Oler, et al., “HMDB 5.0: The Human Metabolome Database for 2022,” Nucleic Acids Research 50, no. D1 (2022): D622-D631.

[143] M. R. Belhaj, N. G. Lawler, N. J. Hoffman, “Metabolomics and Lipidomics: Expanding the Molecular Landscape of Exercise Biology,” Metabolites 11, no. 3 (2021): 151.

[144] B. Xi, H. Gu, H. Baniasadi, D. Raftery, “Statistical Analysis and Modeling of Mass Spectrometry-Based Metabolomics Data,” Methods in Molecular Biology 1198 (2014): 333-353.

[145] P. Castellano-Escuder, R. González-Domínguez, F. Carmona-Pontaque, C. Andrés-Lacueva, A. Sánchez-Pla, “POMAShiny: A User-friendly Web-based Workflow for Metabolomics and Proteomics Data Analysis,” Plos Computational Biology 17, no. 7 (2021): e1009148.

[146] M. Lefouili, K. Nam, “The Evaluation of Bcftools Mpileup and GATK HaplotypeCaller for Variant Calling in Non-human Species,” Scientific Reports 12, no. 1 (2022): 11331.

[147] C. Xu, “A Review of Somatic Single Nucleotide Variant Calling Algorithms for next-generation Sequencing Data,” Computational and Structural Biotechnology Journal 16 (2018): 15-24.

[148] A. L. Gable, D. Szklarczyk, D. Lyon, J. F. Matias Rodrigues, C. von Mering, “Systematic Assessment of Pathway Databases, Based on a Diverse Collection of User-submitted Experiments,” Brief Bioinform 23, no. 5 (2022): bbac355.

[149] D. Masood, L. Ren, C. Nguyen, et al., “Evaluation of Somatic Copy Number Variation Detection by NGS Technologies and Bioinformatics Tools on a Hyper-diploid Cancer Genome,” Genome Biology 25, no. 1 (2024): 163.

[150] A. Merkel, M. Fernández-Callejo, E. Casals, et al., “gemBS: High Throughput Processing for DNA Methylation Data From Bisulfite Sequencing,” Bioinformatics 35, no. 5 (2019): 737-742.

[151] S. Ma, B. Zhang, L. M. LaFave, et al., “Chromatin Potential Identified by Shared Single-Cell Profiling of RNA and Chromatin,” Cell 183, no. 4 (2020): 1103-1116. e20.

[152] G. M. Richter, J. Kruppa, H. G. Keceli, et al., “Epigenetic Adaptations of the Masticatory Mucosa to Periodontal Inflammation,” Clinical Epigenetics 13, no. 1 (2021): 203.

[153] P. L. Baldoni, N. U. Rashid, J. G. Ibrahim, “Efficient Detection and Classification of Epigenomic Changes Under Multiple Conditions,” Biometrics 78, no. 3 (2022): 1141-1154.

[154] M. Vaudel, J. M. Burkhart, R. P. Zahedi, et al., “PeptideShaker Enables Reanalysis of MS-derived Proteomics Data Sets,” Nature Biotechnology 33, no. 1 (2015): 22-24.

[155] D. Szklarczyk, A. L. Gable, K. C. Nastou, et al., “The STRING Database in 2021: Customizable Protein-protein Networks, and Functional Characterization of User-uploaded Gene/Measurement Sets,” Nucleic Acids Research 49, no. D1 (2021): D605-D612.

[156] L. Chen, Q. Li, K. F. A. Nasif, et al., “AI-Driven Deep Learning Techniques in Protein Structure Prediction,” International Journal of Molecular Sciences 25, no. 15 (2024): 8426.

[157] S. Hemmer, S. K. Manier, S. Fischmann, F. Westphal, L. Wagmann, M. R. Meyer, “Comparison of Three Untargeted Data Processing Workflows for Evaluating LC-HRMS Metabolomics Data,” Metabolites 10, no. 9 (2020): 378.

[158] C. Ruttkies, S. Neumann, S. Posch, “Improving MetFrag With Statistical Learning of Fragment Annotations,” BMC Bioinformatics [Electronic Resource] 20, no. 1 (2019): 376.

[159] A. Marco-Ramell, M. Palau-Rodriguez, A. Alay, et al., “Evaluation and Comparison of Bioinformatic Tools for the Enrichment Analysis of Metabolomics Data,” BMC Bioinformatics [Electronic Resource] 19, no. 1 (2018), https://doi.org/10.1186/s12859-017-2006-0.

[160] C. Calderón, L. Rubarth, M. Cebo, I. Merfort, M. Lämmerhofer, “Lipid Atlas of Keratinocytes and Betulin Effects on Its Lipidome Profiled by Comprehensive UHPLC-MS/MS With Data Independent Acquisition Using Targeted Data Processing,” Proteomics 20, no. 11 (2020): e1900113.

[161] D. Wang, Y. Liu, Y. Zhang, et al., “A Real-world Multi-center RNA-seq Benchmarking Study Using the Quartet and MAQC Reference Materials,” Nature Communications 15, no. 1 (2024): 6167.

[162] G. X. Y. Zheng, J. M. Terry, P. Belgrader, et al., “Massively Parallel Digital Transcriptional Profiling of Single Cells,” Nature Communications 8 (2017): 14049.

[163] I. Kalvari, E. P. Nawrocki, J. Argasinska, et al., “Non-Coding RNA Analysis Using the Rfam Database,” CP in Bioinformatics 62, no. 1 (2018): e51.

[164] D. Chakravarty, J. Gao, S. M. Phillips, et al., “OncoKB: A Precision Oncology Knowledge Base,” JCO Precision Oncology 2017 (2017): PO.17.00011.

[165] N. Feizi, S. K. Nair, P. Smirnov, et al., “PharmacoDB 2.0: Improving Scalability and Transparency of in Vitro Pharmacogenomics Analysis,” Nucleic Acids Research 50, no. D1 (2022): D1348-D1357.

[166] X. Wang, L. Chen, W. Liu, et al., “TIMEDB: Tumor Immune Micro-environment Cell Composition Database With Automatic Analysis and Interactive Visualization,” Nucleic Acids Research 51, no. D1 (2023): D1417-D1424.

[167] J. Singer, A. Irmisch, H. J. Ruscheweyh, et al., “Bioinformatics for Precision Oncology,” Briefings in Bioinformatics 20, no. 3 (2019): 778-788.

[168] D. Fernández-Orth, M. Rueda, B. Singh, et al., “A Quality Control Portal for Sequencing Data Deposited at the European Genome-phenome Archive,” Brief Bioinform 23, no. 3 (2022): bbac136.

[169] G. de Sena Brandine, A. D. Smith, “Falco: High-speed FastQC Emulation for Quality Control of Sequencing Data,” F1000Res 8 (2021): 1874.

[170] A. M. Bolger, M. Lohse, B. Usadel, “Trimmomatic: A Flexible Trimmer for Illumina Sequence Data,” Bioinformatics 30, no. 15 (2014): 2114-2120.

[171] X. Zhang, “Highly Effective Batch Effect Correction Method for RNA-seq Count Data,” Computational and Structural Biotechnology Journal 27 (2024): 58-64.

[172] J. T. Leek, W. E. Johnson, H. S. Parker, A. E. Jaffe, J. D. Storey, “The sva Package for Removing Batch Effects and Other Unwanted Variation in High-throughput Experiments,” Bioinformatics 28, no. 6 (2012): 882-883.

[173] J. T. Leek, “svaseq: Removing Batch Effects and Other Unwanted Noise From Sequencing Data,” Nucleic Acids Research 42, no. 21 (2014): e161.

[174] L. Liu, J. Gao, G. Beasley, S. H. Jung, “LASSO and Elastic Net Tend to Over-Select Features,” Mathematics 11, no. 17 (2023): 3738.

[175] R. Tibshirani, “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society: Series B (Methodological) 58, no. 1 (1996): 267-288.

[176] H. Zou, T. Hastie, “Regularization and Variable Selection via the Elastic Net,” Journal of the Royal Statistical Society Series B: Statistical Methodology 67, no. 2 (2005): 301-320.

[177] S. Ng, S. Masarone, D. Watson, M. R. Barnes, “The Benefits and Pitfalls of Machine Learning for Biomarker Discovery,” Cell and Tissue Research 394, no. 1 (2023): 17-31.

[178] S. Uddin, A. Khan, M. E. Hossain, M. A. Moni, “Comparing Different Supervised Machine Learning Algorithms for Disease Prediction,” BMC Medical Informatics and Decision Making 19, no. 1 (2019): 281.

[179] S. Huang, N. Cai, P. P. Pacheco, S. Narrandes, Y. Wang, W. Xu, “Applications of Support Vector Machine (SVM) Learning in Cancer Genomics,” Cancer Genomics & Proteomics 15, no. 1 (2018): 41-51.

[180] J. Martorell-Marugán, S. Tabik, Y. Benhammou, et al. Deep Learning in Omics Data Analysis and Precision Medicine. In: Husi H, ed. Computational (Codon Publications, 2019), Accessed May 1, 2025. http://www.ncbi.nlm.nih.gov/books/NBK550335/.

[181] J. Jovel, R. Greiner, “An Introduction to Machine Learning Approaches for Biomedical Research,” Frontiers in Medicine 8 (2021), https://doi.org/10.3389/fmed.2021.771607.

[182] M. H. Ferrato, A. G. Marsh, K. R. Franke, et al., “Machine Learning Classifier Approaches for Predicting Response to RTK-type-III Inhibitors Demonstrate High Accuracy Using Transcriptomic Signatures and Ex Vivo Data,” Bioinformatics Advances 3, no. 1 (2023): vbad034.

[183] F. Pedregosa, G. Varoquaux, A. Gramfort, et al. Scikit-learn: Machine Learning in Python. Published online June 5, 2018.

[184] O. C. Novac, M. C. Chirodea, C. M. Novac, et al., “Analysis of the Application Efficiency of TensorFlow and PyTorch in Convolutional Neural Network,” Sensors (Basel) 22, no. 22 (2022): 8872.

[185] L. Rampasek, A. Goldenberg, “TensorFlow: Biology's Gateway to Deep Learning?,” Cell Systems 2, no. 1 (2016): 12-14.

[186] L. J. Marcos-Zambrano, V. M. López-Molina, B. Bakir-Gungor, “A Toolbox of Machine Learning Software to Support Microbiome Analysis,” Frontiers in Microbiology 14 (2023): 1250806.

[187] L. Chen, C. Wang, H. Sun, et al., “The Bioinformatics Toolbox for circRNA Discovery and Analysis,” Brief Bioinform 22, no. 2 (2020): 1706-1728.

[188] T. D. Tran, M. T. Nguyen, “C-Biomarker.Net: A Cytoscape App for the Identification of Cancer Biomarker Genes From Cores of Large Biomolecular Networks,” Bio Systems 226 (2023): 104887.

[189] G. Su, J. H. Morris, B. Demchak, G. D. Bader, “Biological Network Exploration With Cytoscape 3,” CP in Bioinformatics 47 (2014): 8.13.1-24.

[190] C. Jia, T. Wang, D. Cui, et al., “A Metagene Based Similarity Network Fusion Approach for Multi-omics Data Integration Identified Novel Subtypes in Renal Cell Carcinoma,” Brief Bioinform 25, no. 6 (2024): bbae606.

[191] M. Chierici, N. Bussola, A. Marcolini, et al., “Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling,” Frontiers in Oncology 10 (2020): 1065.

[192] Q. Mo, R. Shen, C. Guo, M. Vannucci, K. S. Chan, S. G. Hilsenbeck, “A Fully Bayesian Latent Variable Model for Integrative Clustering Analysis of Multi-type Omics Data,” Biostatistics (Oxford, England) 19, no. 1 (2018): 71-86.

[193] G. Zhang, Z. Peng, C. Yan, J. Wang, J. Luo, H. Luo, “. MultiGATAE: A Novel Cancer Subtype Identification Method Based on Multi-Omics and Attention Mechanism,” Frontiers in Genetics 13 (2022): 855629.

[194] E. M. Liu, A. Luna, G. Dong, C. Sander, “netboxr: Automated Discovery of Biological Process Modules by Network Analysis in R,” PLoS ONE 15, no. 11 (2020): e0234669.

[195] J. Reimand, R. Isser, V. Voisin, et al., “Pathway Enrichment Analysis and Visualization of Omics Data Using G:Profiler, GSEA, Cytoscape and EnrichmentMap,” Nature Protocols 14, no. 2 (2019): 482-517.

[196] G. Dennis, B. T. Sherman, D. A. Hosack, et al., “DAVID: Database for Annotation, Visualization, and Integrated Discovery,” Genome Biology 4, no. 5 (2003): P3.

[197] DAVID Functional Annotation Bioinformatics Microarray Analysis. Accessed May 1, 2025. https://davidbioinformatics.nih.gov/

[198] A. Fabregat, K. Sidiropoulos, G. Viteri, et al., “Reactome Pathway Analysis: A High-performance in-memory Approach,” BMC Bioinformatics [Electronic Resource] 18, no. 1 (2017): 142.

[199] Home—Reactome Pathway Database. Accessed May 1, 2025. https://reactome.org/

[200] A. Krämer, J. Green, J. Pollard, S. Tugendreich, “Causal Analysis Approaches in Ingenuity Pathway Analysis,” Bioinformatics 30, no. 4 (2014): 523-530.

[201] R. Oughtred, J. Rust, C. Chang, et al., “The BioGRID Database: A Comprehensive Biomedical Resource of Curated Protein, Genetic, and Chemical Interactions,” Protein Science 30, no. 1 (2021): 187-200.

[202] T. Wu, E. Hu, S. Xu, et al., “clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data,” Innovation (Camb) 2, no. 3 (2021): 100141.

[203] S. Patkar, A. Magen, R. Sharan, S. Hannenhalli, “A Network Diffusion Approach to Inferring Sample-specific Function Reveals Functional Changes Associated With Breast Cancer,” PLOS Computational Biology 13, no. 11 (2017): e1005793.

[204] J. Montojo, K. Zuberi, H. Rodriguez, et al., “GeneMANIA Cytoscape Plugin: Fast Gene Function Predictions on the Desktop,” Bioinformatics 26, no. 22 (2010): 2927-2928.

[205] Y. Zhou, B. Zhou, L. Pache, et al., “Metascape Provides a Biologist-oriented Resource for the Analysis of Systems-level Datasets,” Nature Communications 10, no. 1 (2019): 1523.

[206] K. D. Davis, N. Aghaeepour, A. H. Ahn, et al., “Discovery and Validation of Biomarkers to Aid the Development of Safe and Effective Pain Therapeutics: Challenges and Opportunities,” Nature Reviews Neurology 16, no. 7 (2020): 381-400.

[207] A. Sadlon, “In Silico Models to Validate Novel Blood-Based Biomarkers,” Methods in Molecular Biology 2785 (2024): 321-344.

[208] L. Venkataramana, S. G. Jacob, S. Saraswathi, D. Venkata Vara Prasad, “Identification of Common and Dissimilar Biomarkers for Different Cancer Types From Gene Expressions of RNA-sequencing Data,” Gene Reports 19 (2020): 100654.

[209] Z. Huang, T. Lan, J. Wang, Z. Chen, X. Zhang, “Identification and Validation of Seven RNA Binding Protein Genes as a Prognostic Signature in Oral Cavity Squamous Cell Carcinoma,” Bioengineered 12, no. 1 (2021): 7248-7262.

[210] R. Edgar, M. Domrachev, A. E. Lash, “Gene Expression Omnibus: NCBI Gene Expression and Hybridization Array Data Repository,” Nucleic Acids Research 30, no. 1 (2002): 207-210.

[211] J. Zhang, J. Baran, A. Cros, et al., “International Cancer Genome Consortium Data Portal-a One-stop Shop for Cancer Genomics Data,” Database (Oxford) 2011 (2011): bar026.

[212] S. Kim, C. W. Lin, Tseng GeorgeC, “MetaKTSP: A Meta-analytic Top Scoring Pair Method for Robust Cross-study Validation of Omics Prediction Analysis,” Bioinformatics 32, no. 13 (2016): 1966-1973.

[213] S. U. Khan, “Composite to Clarity: Shifting from Combined to Individual Endpoints in Meta-Analyses of Cardiovascular Outcome Trials,” JACC: Advances 2, no. 7 (2023): 100548.

[214] X. Cheng, Y. Liu, J. Wang, et al., “cSurvival: A Web Resource for Biomarker Interactions in Cancer Outcomes and in Cell Lines,” Brief Bioinform 23, no. 3 (2022): bbac090.

[215] R. Aguirre-Gamboa, H. Gomez-Rueda, E. Martínez-Ledesma, “SurvExpress: An Online Biomarker Validation Tool and Database for Cancer Gene Expression Data Using Survival Analysis,” PLoS ONE 8, no. 9 (2013): e74250.

[216] CRAN: Package survminer. Accessed May 1, 2025. https://cran.r-project.org/web/packages/survminer/index.html

[217] K. Jóźwiak, V. H. Nguyen, L. Sollfrank, S. C. Linn, M. Hauptmann, “Cox Proportional Hazards Regression in Small Studies of Predictive Biomarkers,” Scientific Reports 14, no. 1 (2024): 14232.

[218] Z. Tang, B. Kang, C. Li, T. Chen, Z. Zhang, “GEPIA2: An Enhanced Web Server for Large-scale Expression Profiling and Interactive Analysis,” Nucleic Acids Research 47, no. W1 (2019): W556-W560.

[219] T. Li, J. Fan, B. Wang, et al., “TIMER: A Web Server for Comprehensive Analysis of Tumor-infiltrating Immune Cells,” Cancer Research 77, no. 21 (2017): e108-e110.

[220] J. Anaya, “OncoLnc: Linking TCGA Survival Data to mRNAs, miRNAs, and lncRNAs,” PeerJ Computer Science 2 (2016): e67.

[221] H. Mizuno, K. Kitada, K. Nakai, A. Sarai, “PrognoScan: A New Database for Meta-analysis of the Prognostic Value of Genes,” BMC Medical Genomics 2 (2009): 18.

[222] Biomarker Discovery and Validation Using a Combination of In Vitro and In Vivo Studies. Accessed May 1, 2025. https://blog.crownbio.com/biomarker-discovery-and-validation-combining-in-vitro-and-in-vivo-studies

[223] M. A. Hossain, M. Sohel, M. H. Rahman, et al., “Bioinformatics and in Silico Approaches to Identify Novel Biomarkers and Key Pathways for Cancers That Are Linked to the Progression of Female Infertility: A Comprehensive Approach for Drug Discovery,” PLoS ONE 18, no. 1 (2023): e0265746.

[224] M. Mann, C. Kumar, W. F. Zeng, M. T. Strauss, “Artificial Intelligence for Proteomics and Biomarker Discovery,” Cell Systems 12, no. 8 (2021): 759-770.

[225] F. S. Ou, S. Michiels, Y. Shyr, A. A. Adjei, A. L. Oberg, “Biomarker Discovery and Validation: Statistical Considerations,” Journal of Thoracic Oncology 16, no. 4 (2021): 537-545.

[226] O. Alter, S. P. Ponnapalli, J. W. Tsai, et al., “Prospective Validation From a Retrospective Trial That Validated an AI/ML-derived Whole-genome Biomarker as the Most Accurate and Precise Predictor of Survival and Response to Treatment in Glioblastoma,” JCO 42, no. 16_suppl (2024): e14028-e14028.

[227] M. M. Boyiadzis, J. M. Kirkwood, J. L. Marshall, C. C. Pritchard, N. S. Azad, J. L. Gulley, “Significance and Implications of FDA Approval of pembrolizumab for Biomarker-defined Disease,” Journal for ImmunoTherapy of Cancer 6, no. 1 (2018): 35.

[228] D. T. Le, J. N. Uram, H. Wang, et al., “Programmed Death-1 Blockade in Mismatch Repair Deficient Colorectal Cancer,” Journal of Clinical Oncology 34, no. 15 (2016), https://doi.org/10.1200/JCO.2016.34.15_suppl.103. Published online May 20, 2016.

[229] R. Rosell, E. Carcereny, R. Gervais, et al., “Erlotinib versus Standard Chemotherapy as First-line Treatment for European Patients With Advanced EGFR Mutation-positive Non-small-cell Lung Cancer (EURTAC): A Multicentre, Open-label, Randomised Phase 3 Trial,” The Lancet Oncology 13, no. 3 (2012): 239-246.

[230] National Cancer Institute (NCI). MARVEL: Marker Validation of Erlotinib in Lung Cancer- A Phase III Biomarker Validation Study of Second-Line Therapy in Patients with Advanced Non-Small Cell Lung Cancer (NSCLC) Randomized to Pemetrexed versus Erlotinib. Clinicaltrials.Gov; 2015. Accessed May 1, 2025. https://clinicaltrials.gov/study/NCT00738881

[231] R. S. Herbst, M. W. Redman, E. S. Kim, et al., “Cetuximab plus Carboplatin and Paclitaxel With or Without Bevacizumab versus Carboplatin and Paclitaxel With or Without Bevacizumab in Advanced NSCLC (SWOG S0819): A Randomised, Phase 3 Study,” The Lancet Oncology 19, no. 1 (2018): 101-114.

[232] J. Li, S. Long, Z. Yang, et al., “Single-cell Transcriptomics Reveals IRF7 Regulation of the Tumor Microenvironment in Isocitrate Dehydrogenase Wild-type Glioma,” MedComm 5, no. 11 (2024): e754.

[233] V. Janitri, K. N. ArulJothi, V. M. Ravi Mythili, et al., “The Roles of Patient-derived Xenograft Models and Artificial Intelligence Toward Precision Medicine,” MedComm 5, no. 10 (2024): e745.

[234] H. Xu, Z. Fan, S. Jiang, et al., “Integrating Multiplex Immunohistochemistry and Machine Learning for Glioma Subtyping and Prognosis Prediction,” MedComm 6, no. 5 (2025): e70138.

[235] A. J. Bagchee-Clark, E. J. Mucaki, T. Whitehead, P. K. Rogan, “Pathway-extended Gene Expression Signatures Integrate Novel Biomarkers That Improve Predictions of Patient Responses to Kinase Inhibitors,” MedComm 1, no. 3 (2020): 311-327.

[236] V. Bhardwaj, A. Sharma, S. V. Parambath, et al., “Machine Learning for Endometrial Cancer Prediction and Prognostication,” Frontiers in Oncology 12 (2022): 852746.

[237] G. Tanzhu, L. Chen, J. Ning, et al., “Metastatic Brain Tumors: From Development to Cutting-edge Treatment,” MedComm 6, no. 1 (2024): e70020.

[238] Y. Yu, G. Cai, R. Lin, et al., “Multimodal Data Fusion AI Model Uncovers Tumor Microenvironment Immunotyping Heterogeneity and Enhanced Risk Stratification of Breast Cancer,” MedComm 5, no. 12 (2024): e70023.

[239] A. Al-Dherasi, Q. T. Huang, Y. Liao, et al., “A Seven-gene Prognostic Signature Predicts Overall Survival of Patients With Lung Adenocarcinoma (LUAD),” Cancer Cell International 21, no. 1 (2021): 294.

[240] Y. Huang, P. Zeng, C. Zhong, “Classifying Breast Cancer Subtypes on Multi-omics Data via Sparse Canonical Correlation Analysis and Deep Learning,” BMC Bioinformatics [Electronic Resource] 25, no. 1 (2024): 132.

[241] H. Chai, Y. Huang, L. Xu, X. Song, M. He, Q. Wang, “A Decentralized Federated Learning-based Cancer Survival Prediction Method With Privacy Protection,” Heliyon 10, no. 11 (2024): e31873.

[242] F. Zhu, R. Zhong, F. Li, et al., “Development and Validation of a Deep Transfer Learning-based Multivariable Survival Model to Predict Overall Survival in Lung Cancer,” Translational Lung Cancer Research 12, no. 3 (2023): 471-482.

[243] T. Khater, A. Hussain, S. Mahmoud, S. Yasen. Explainable AI for Breast Cancer Detection: A LIME-Driven Approach. 2023 16th International Conference on Developments in eSystems Engineering (DeSE). Published online December 18, 2023: 540-545. https://doi.org/10.1109/DeSE60595.2023.10469341

[244] M. Arnold, “Integrating Multi-omics Data for Target and Biomarker Discovery,” Alzheimers Dement 20, no. Suppl 1 (2025): e086331.

[245] Z. Wang, X. Sui, W. Song, et al., “Reinforcement Learning for Individualized Lung Cancer Screening Schedules: A Nested Case-control Study,” Cancer Medicine 13, no. 13 (2024): e7436.

[246] A. Mahmoud, E. Takaoka, “An Enhanced Machine Learning Approach With Stacking Ensemble Learner for Accurate Liver Cancer Diagnosis Using Feature Selection and Gene Expression Data,” Healthcare Analytics 7 (2025): 100373.

[247] W. Gu, X. Yang, M. Yang, K. Han, W. Pan, Z. Zhu, “MarkerGenie: An NLP-enabled Text-mining System for Biomedical Entity Relation Extraction,” Bioinformatics Advances 2, no. 1 (2022): vbac035.

[248] Z. Wang, Y. Zhou, T. Takagi, J. Song, Y. S. Tian, T. Shibuya, “Genetic Algorithm-based Feature Selection With Manifold Learning for Cancer Classification Using Microarray Data,” BMC Bioinformatics [Electronic Resource] 24, no. 1 (2023): 139.

[249] Z. Momeni, E. Hassanzadeh, M. Saniee Abadeh, R. Bellazzi, “A Survey on Single and Multi Omics Data Mining Methods in Cancer Data Classification,” Journal of Biomedical Informatics 107 (2020): 103466.

[250] Y. Wang, W. Hou, N. Sheng, et al., “Graph Pooling in Graph Neural Networks: Methods and Their Applications in Omics Studies,” Artificial Intelligence Review 57, no. 11 (2024): 294.

[251] T. Kolisnik, F. Keshavarz-Rahaghi, R. V. Purcell, A. N. H. Smith, O. K. Silander, “pyRforest: A Comprehensive R Package for Genomic Data Analysis Featuring scikit-learn Random Forests in R,” Briefings in Functional Genomics 24 (2025): elae038.

[252] D. Ruiz-Perez, J. Lugo-Martinez, N. Bourguignon, et al., “Dynamic Bayesian Networks for Integrating Multi-omics Time Series Microbiome Data,” Msystems 6, no. 2 (2021), https://doi.org/10.1128/mSystems.01105-20. e01105-20.

[253] F. H. Yagin, R. El Shawi, A. Algarni, C. Colak, F. Al-Hashem, L. P. Ardigò, “Metabolomics Biomarker Discovery to Optimize Hepatocellular Carcinoma Diagnosis: Methodology Integrating AutoML and Explainable Artificial Intelligence,” Diagnostics (Basel) 14, no. 18 (2024): 2049.

[254] R. Cuevas-Diaz Duran, J. C. González-Orozco, I. Velasco, J. Q. Wu, “Single-cell and Single-nuclei RNA Sequencing as Powerful Tools to Decipher Cellular Heterogeneity and Dysregulation in Neurodegenerative Diseases,” Frontiers in Cell and Developmental Biology 10 (2022): 884748.

[255] N. Erfanian, A. A. Heydari, A. M. Feriz, et al., “Deep Learning Applications in Single-cell Genomics and Transcriptomics Data Analysis,” Biomedicine & Pharmacotherapy 165 (2023): 115077.

[256] D. M. Koh, N. Papanikolaou, U. Bick, et al., “Artificial Intelligence and Machine Learning in Cancer Imaging,” Communications Medicine (London) 2 (2022): 133.

[257] B. Yuan, D. Yang, B. E. G. Rothberg, H. Chang, T. Xu, “Unsupervised and Supervised Learning With Neural Network for human Transcriptome Analysis and Cancer Diagnosis,” Scientific Reports 10, no. 1 (2020): 19106.

[258] D. Bertsimas, H. Wiberg, “Machine Learning in Oncology: Methods, Applications, and Challenges,” JCO Clinical Cancer Informatics 4 (2020), https://doi.org/10.1200/CCI.20.00072. CCI.20.00072.

[259] S. Steyaert, M. Pizurica, D. Nagaraj, et al., “Multimodal Data Fusion for Cancer Biomarker Discovery With Deep Learning,” Nature Machine Intelligence 5, no. 4 (2023): 351-362.

[260] C. Greeley, L. Holder, E. E. Nilsson, M. K. Skinner, “Scalable Deep Learning Artificial Intelligence Histopathology Slide Analysis and Validation,” Scientific Reports 14, no. 1 (2024): 26748.

[261] C. Wemmert, J. Weber, F. Feuerhake, G. Forestier. Deep Learning for Histopathological Image Analysis. In: Elloumi M, ed. “Deep Learning for Biomedical Data Analysis: Techniques, Approaches, and Applications” (Springer International Publishing, 2021): 153-169, https://doi.org/10.1007/978-3-030-71676-9_7.

[262] E. U. Alum, “AI-driven Biomarker Discovery: Enhancing Precision in Cancer Diagnosis and Prognosis,” Discover Oncology 16, no. 1 (2025): 313.

[263] M. Kumaran, U. Subramanian, B. Devarajan, “Performance Assessment of Variant Calling Pipelines Using human Whole Exome Sequencing and Simulated Data,” BMC Bioinformatics [Electronic Resource] 20, no. 1 (2019): 342.

[264] P. Kamya, I. V. Ozerov, F. W. Pun, et al., “PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery,” Journal of Chemical Information and Modeling 64, no. 10 (2024): 3961-3969. Published online February 26, 2024.

[265] T. M. Nguyen, N. Kim, D. H. Kim, et al., “Deep Learning for Human Disease Detection, Subtype Classification, and Treatment Response Prediction Using Epigenomic Data,” Biomedicines 9, no. 11 (2021): 1733.

[266] W. Lotter, M. J. Hassett, N. Schultz, K. L. Kehl, E. M. Van Allen, E. Cerami, “Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions,” Cancer Discovery 14, no. 5 (2024): 711-726.

[267] W. L. Bi, A. Hosny, M. B. Schabath, et al., “Artificial Intelligence in Cancer Imaging: Clinical Challenges and Applications,” CA: A Cancer Journal for Clinicians 69, no. 2 (2019): 127-157.

[268] M. Ennab, H. Mcheick, “Enhancing Interpretability and Accuracy of AI Models in Healthcare: A Comprehensive Review on Challenges and Future Directions,” Front Robot AI 11 (2024): 1444763.

[269] C. Mennella, U. Maniscalco, G. De Pietro, M. Esposito, “Ethical and Regulatory Challenges of AI Technologies in Healthcare: A Narrative Review,” Heliyon 10, no. 4 (2024): e26297.

[270] S. Yu, S. S. Lee, H. Hwang, “The Ethics of Using Artificial Intelligence in Medical Research,” KMJ 39, no. 4 (2024): 229-237.

[271] C. J. Creighton, “Making Use of Cancer Genomic Databases,” Current Protocols in Molecular Biology 121 (2018): 19.14.1-19.14.13.

[272] K. Tomczak, P. Czerwińska, M. Wiznerowicz, “The Cancer Genome Atlas (TCGA): An Immeasurable Source of Knowledge,” Contemporary Oncology (Pozn) 19, no. 1A (2015): A68-A77.

[273] International Network of Cancer Genome Projects. Nature 2010; 464(7291): 993-998.

[274] A. A. Gazola, W. Lautert-Dutra, L. F. Archangelo, R. B. dos Reis, J. A. Squire, “Precision Oncology Platforms: Practical Strategies for Genomic Database Utilization in Cancer Treatment,” Molecular Cytogenetics 17, no. 1 (2024): 28.

[275] J. G. Tate, S. Bamford, H. C. Jubb, et al., “COSMIC: The Catalogue of Somatic Mutations in Cancer,” Nucleic Acids Research 47, no. Database issue (2019): D941-D947.

[276] M. Tarailo-Graovac, J. Y. A. Zhu, A. Matthews, C. D. M. van Karnebeek, W. W. Wasserman, “Assessment of the ExAC Data Set for the Presence of Individuals With Pathogenic Genotypes Implicated in Severe Mendelian Pediatric Disorders,” Genetics in Medicine 19, no. 12 (2017): 1300-1308.

[277] W. Zhang, X. Xie, Z. Huang, et al., “The Integration of Single-cell Sequencing, TCGA, and GEO Data Analysis Revealed That PRRT3-AS1 Is a Biomarker and Therapeutic Target of SKCM,” Frontiers in immunology 13 (2022): 919145.

[278] R. Lowe, N. Shirley, M. Bleackley, S. Dolan, T. Shafee, “Transcriptomics Technologies,” Plos Computational Biology 13, no. 5 (2017): e1005457.

[279] Y. Perez-Riverol, J. Bai, C. Bandla, et al., “The PRIDE Database Resources in 2022: A Hub for Mass Spectrometry-based Proteomics Evidences,” Nucleic Acids Research 50, no. D1 (2022): D543-D552.

[280] S. Killcoyne, E. W. Deutsch, J. Boyle, “Mining PeptideAtlas for Biomarkers and Therapeutics in human Disease,” Current Pharmaceutical Design 18, no. 6 (2012): 748-754. Accessed May 2, 2025. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4361040/.

[281] M. Vaudel, K. Verheggen, A. Csordas, et al., “Exploring the Potential of Public Proteomics Data,” Proteomics 16, no. 2 (2016): 214-225.

[282] O. Yurekten, T. Payne, N. Tejera, et al., “MetaboLights: Open Data Repository for Metabolomics,” Nucleic Acids Research 52, no. D1 (2024): D640-D646.

[283] M. J. Goldman, B. Craft, M. Hastie, et al., “Visualizing and Interpreting Cancer Genomics Data via the Xena Platform,” Nature Biotechnology 38, no. 6 (2020): 675-678.

[284] M. V. Dieci, F. Miglietta, G. Griguolo, V. Guarneri, “Biomarkers for HER2-positive Metastatic Breast Cancer: Beyond Hormone Receptors,” Cancer Treatment Reviews 88 (2020): 102064.

[285] S. Garg, A. Sachdeva, M. Peeters, J. McClements, “Point-of-Care Prostate Specific Antigen Testing: Examining Translational Progress Toward Clinical Implementation,” ACS Sens 8, no. 10 (2023): 3643-3658.

[286] P. Charkhchi, C. Cybulski, J. Gronwald, F. O. Wong, S. A. Narod, M. R. Akbari, “CA125 and Ovarian Cancer: A Comprehensive Review,” Cancers (Basel) 12, no. 12 (2020): 3730.

[287] P. T. Harrison, S. Vyse, P. H. Huang, “Rare Epidermal Growth Factor Receptor (EGFR) Mutations in Non-small Cell Lung Cancer,” Seminars in Cancer Biology 61 (2020): 167-179.

[288] L. Tembuyser, M. J. L. Ligtenberg, N. Normanno, S. Delen, J. H. van Krieken, E. M. C. Dequeker, “Higher Quality of Molecular Testing, an Unfulfilled Priority: Results From External Quality Assessment for KRAS Mutation Testing in Colorectal Cancer,” The Journal of Molecular Diagnostics 16, no. 3 (2014): 371-377.

[289] G. Castellani, M. Buccarelli, M. B. Arasi, et al., “BRAF Mutations in Melanoma: Biological Aspects, Therapeutic Implications, and Circulating Biomarkers,” Cancers (Basel) 15, no. 16 (2023): 4026.

[290] H. Hanif, M. J. Ali, A. T. Susheela, et al., “Update on the Applications and Limitations of Alpha-fetoprotein for Hepatocellular Carcinoma,” World Journal of Gastroenterology 28, no. 2 (2022): 216-229.

[291] R. Tenchov, A. K. Sapra, J. Sasso, et al., “Biomarkers for Early Cancer Detection: A Landscape View of Recent Advancements, Spotlighting Pancreatic and Liver Cancers,” ACS Pharmacology & Translational Science 7, no. 3 (2024): 586-613.

[292] A. Ooki, H. Osumi, K. Yoshino, K. Yamaguchi, “Potent Therapeutic Strategy in Gastric Cancer With Microsatellite Instability-high and/or Deficient Mismatch Repair,” Gastric Cancer 27, no. 5 (2024): 907-931.

[293] S. H. Yu, S. S. Kim, S. Kim, H. Lee, T. W. Kang, “FGFR3 Mutations in Urothelial Carcinoma: A Single-Center Study Using Next-Generation Sequencing,” Journal of Clinical Medicine 13, no. 5 (2024): 1305.

[294] E. De Braekeleer, N. Douet-Guilbert, M. De Braekeleer, “RARA Fusion Genes in Acute Promyelocytic Leukemia: A Review,” Expert Review of Hematology 7, no. 3 (2014): 347-357.

[295] S. Brandner, A. McAleenan, C. Kelly, et al., “MGMT Promoter Methylation Testing to Predict Overall Survival in People With Glioblastoma Treated With Temozolomide: A Comprehensive Meta-analysis Based on a Cochrane Systematic Review,” Neuro-Oncology 23, no. 9 (2021): 1457-1469.

[296] M. Bartosik, L. Moranova, N. Izadi, et al., “Advanced Technologies towards Improved HPV Diagnostics,” Journal of Medical Virology 96, no. 2 (2024): e29409.

[297] G. Rosas, R. Ruiz, J. M. Araujo, J. A. Pinto, L. Mas, “ALK Rearrangements: Biology, Detection and Opportunities of Therapy in Non-small Cell Lung Cancer,” Critical Reviews in Oncology/Hematology 136 (2019): 48-55.

[298] D. De Novellis, R. Fontana, A. Carobene, et al., “Serum Free Light-Chain Ratio at Diagnosis Is Associated With Early Renal Damage in Multiple Myeloma: A Case Series Real-World Study,” Biomedicines 10, no. 7 (2022): 1657.

[299] B. Arun, F. J. Couch, J. Abraham, N. Tung, P. A. Fasching, “BRCA-mutated Breast Cancer: The Unmet Need, Challenges and Therapeutic Benefits of Genetic Testing,” British Journal of Cancer 131, no. 9 (2024): 1400-1414.

[300] D. B. Doroshow, S. Bhalla, M. B. Beasley, et al., “PD-L1 as a Biomarker of Response to Immune-checkpoint Inhibitors,” Nature reviews Clinical oncology 18, no. 6 (2021): 345-362.

[301] M. Huang, S. Yang, W. C. S. Tai, et al., “Bioinformatics Identification of Regulatory Genes and Mechanism Related to Hypoxia-Induced PD-L1 Inhibitor Resistance in Hepatocellular Carcinoma,” International Journal of Molecular Sciences 24, no. 10 (2023): 8720.

[302] D. Hammerl, J. W. M. Martens, M. Timmermans, et al., “Spatial Immunophenotypes Predict Response to Anti-PD1 Treatment and Capture Distinct Paths of T Cell Evasion in Triple Negative Breast Cancer,” Nature Communications 12, no. 1 (2021): 5668.

[303] N. M. Tung, J. E. Garber, “BRCA1/2 testing: Therapeutic Implications for Breast Cancer Management,” British Journal of Cancer 119, no. 2 (2018): 141-152.

[304] R. L. Siegel, A. N. Giaquinto, A. Jemal, “Cancer Statistics, 2024,” CA: A Cancer Journal for Clinicians 74, no. 1 (2024): 12-49.

[305] A. N. Giaquinto, H. Sung, L. A. Newman, et al., “Breast Cancer Statistics 2024,” CA: A Cancer Journal for Clinicians 74, no. 6 (2024): 477-495.

[306] V. Bhardwaj, X. Zhang, V. Pandey, M. Garg, “Neo-vascularization-based Therapeutic Perspectives in Advanced Ovarian Cancer,” Biochimica et Biophysica Acta: Reviews on Cancer 1878, no. 3 (2023): 188888.

[307] Á. Bartha, B. Győrffy, “Comprehensive Outline of Whole Exome Sequencing Data Analysis Tools Available in Clinical Oncology,” Cancers (Basel) 11, no. 11 (2019): 1725.

[308] W. McLaren, L. Gil, S. E. Hunt, et al., “The Ensembl Variant Effect Predictor,” Genome Biology 17, no. 1 (2016): 122.

[309] L. Incorvaia, C. Marchetti, C. Brando, et al., “BRCA Functional Domains Associated With High Risk of Multiple Primary Tumors and Domain-related Sensitivity to olaparib: The Prometheus Study,” ESMO Open 10, no. 2 (2025): 104076.

[310] S. Obayashi, M. Aoki, K. Tanabe, et al., “The Role of BRCA1/2 Genetic Testing in Perioperative Breast Cancer Management: Advancing Shared Decision-making and Personalized Care,” International Journal of Clinical Oncology (2025). Published online April 29, https://doi.org/10.1007/s10147-025-02773-7.

[311] B. Zou, V. H. F. Lee, H. Yan, “Prediction of Sensitivity to Gefitinib/Erlotinib for EGFR Mutations in NSCLC Based on Structural Interaction Fingerprints and Multilinear Principal Component Analysis,” BMC Bioinformatics [Electronic Resource] 19, no. 1 (2018): 88.

[312] J. Martín-Arana, F. Gimeno-Valiente, T. V. Henriksen, et al., “Whole-exome Tumor-agnostic ctDNA Analysis Enhances Minimal Residual Disease Detection and Reveals Relapse Mechanisms in Localized Colon Cancer,” Nat Cancer (2025): 1-17, Published online April 29, https://doi.org/10.1038/s43018-025-00960-z.

[313] S. Halder, S. Basu, S. Lal, A. K. Ganti, S. K. Batra, P. Seshacharyulu, “Targeting the EGFR Signaling Pathway in Cancer Therapy: What's New in 2023?,” Expert Opinion on Therapeutic Targets 27, no. 4-5 (2023): 305-324.

[314] G. Hamilton, B. Rath, A. Plangger, M. Hochmair, “Implementation of Functional Precision Medicine for Anaplastic Lymphoma Kinase-rearranged Non-small Lung Cancer,” Precision Cancer Medicine 2, no. 0 (2019),

[315] I. B. Muller, Langen AJ de, E. Giovannetti, G. J. Peters, “Anaplastic Lymphoma Kinase Inhibition in Metastatic Non-small Cell Lung Cancer: Clinical Impact of alectinib,” Ottawa 10 (2017): 4535-4541.

[316] G. Zakharova, M. Suntsova, E. Rabushko, et al., “A New Approach of Detecting ALK Fusion Oncogenes by RNA Sequencing Exon Coverage Analysis,” Cancers 16, no. 22 (2024): 3851.

[317] G. Chazan, F. Franchini, R. Shah, et al., “Real-World Treatment and Outcomes in ALK-Rearranged NSCLC: Results from a Large U.S.-Based Database,” JTO Clinical and Research Reports 5, no. 8 (2024): 100662.

[318] X. N. Wang, S. J. Wang, V. Pandey, et al., “Trefoil Factor 3 as a Novel Biomarker to Distinguish between Adenocarcinoma and Squamous Cell Carcinoma,” Medicine 94, no. 20 (2015): e860.

[319] V. Pandey, Z. S. Wu, M. Zhang, et al., “Trefoil Factor 3 Promotes Metastatic Seeding and Predicts Poor Survival Outcome of Patients With Mammary Carcinoma,” Breast Cancer Research 16 (2014): 429.

[320] F. Cheng, X. Wang, Y. S. Chiou, et al., “Trefoil Factor 3 Promotes Pancreatic Carcinoma Progression via WNT Pathway Activation Mediated by Enhanced WNT Ligand Expression,” Cell Death & Disease 13, no. 3 (2022): 265.

[321] V. Pandey, M. Zhang, Q. Y. Chong, et al., “Hypomethylation Associated Enhanced Transcription of Trefoil Factor-3 Mediates Tamoxifen-stimulated Oncogenicity of ER+ Endometrial Carcinoma Cells,” Oncotarget 8, no. 44 (2017): 77268-77291.

[322] M. L. You, Y. J. Chen, Q. Y. Chong, et al., “Trefoil Factor 3 Mediation of Oncogenicity and Chemoresistance in Hepatocellular Carcinoma Is AKT-BCL-2 Dependent,” Oncotarget 8, no. 24 (2017): 39323-39344.

[323] N. L. C. Bui, V. Pandey, T. Zhu, L. Ma, P. E. Basappa Lobie, “Bad Phosphorylation as a Target of Inhibition in Oncology,” Cancer Letters 415 (2018): 177-186.

[324] V. Pandey, B. Wang, C. D. Mohan, et al., “Discovery of a Small-molecule Inhibitor of Specific Serine Residue BAD Phosphorylation,” PNAS 115, no. 44 (2018): E10505-E10514.

[325] R. M. Chen, Y. S. Chiou, Q. Y. Chong, et al., “Pharmacological Inhibition of TFF3 Enhances Sensitivity of CMS4 Colorectal Carcinoma to 5-Fluorouracil Through Inhibition of p44/42 MAPK,” International Journal of Molecular Sciences 20, no. 24 (2019): 6215.

[326] M. Zhang, B. Wang, Q. Y. Chong, et al., “A Novel Small-molecule Inhibitor of Trefoil Factor 3 (TFF3) Potentiates MEK1/2 Inhibition in Lung Adenocarcinoma,” Oncogenesis 8, no. 11 (2019): 65.

[327] Y. Wang, Y. S. Chiou, Q. Y. Chong, et al., “Pharmacological Inhibition of BAD Ser99 Phosphorylation Enhances the Efficacy of Cisplatin in Ovarian Cancer by Inhibition of Cancer Stem Cell-Like Behavior,” ACS Pharmacology & Translational Science 3, no. 6 (2020): 1083-1099.

[328] V. Pandey, X. Zhang, H. M. Poh, et al., “Monomerization of Homodimeric Trefoil Factor 3 (TFF3) by an Aminonitrile Compound Inhibits TFF3-Dependent Cancer Cell Survival,” ACS Pharmacology & Translational Science 5, no. 9 (2022): 761-773.

[329] X. Zhang, L. Wang, S. Chen, et al., “Combined Inhibition of BADSer99 Phosphorylation and PARP Ablates Models of Recurrent Ovarian Carcinoma,” Communications Medicine (London) 2 (2022): 82.

[330] H. Guo, Y. Q. Tan, X. Huang, et al., “Small Molecule Inhibition of TFF3 Overcomes Tamoxifen Resistance and Enhances Taxane Efficacy in ER+ Mammary Carcinoma,” Cancer Letters 579 (2023): 216443.

[331] X. Zhang, P. Huang, L. Wang, et al., “Inhibition of BAD-Ser99 Phosphorylation Synergizes With PARP Inhibition to Ablate PTEN-deficient Endometrial Carcinoma,” Cell Death & Disease 13, no. 6 (2022): 558.

[332] Y. Q. Tan, Y. S. Chiou, H. Guo, et al., “Vertical Pathway Inhibition of Receptor Tyrosine Kinases and BAD With Synergistic Efficacy in Triple Negative Breast Cancer,” Npj Precision Oncology 8 (2024): 8.

[333] L. Wang, X. Zhang, S. Chen, et al., “Combining Mitomycin C With Inhibition of BAD Phosphorylation Enhances Apoptotic Cell Death in Advanced Cervical Cancer,” Translational Oncology 49 (2024): 102103.

[334] Y. Q. Tan, B. Sun, X. Zhang, et al., “Concurrent Inhibition of pBADS99 Synergistically Improves MEK Inhibitor Efficacy in KRASG12D-mutant Pancreatic Ductal Adenocarcinoma,” Cell Death & Disease 15, no. 2 (2024): 173.

[335] C. He, X. Wang, Y. S. Chiou, et al., “Inhibition of TFF3 Synergizes With c-MET Inhibitors to Decrease the CSC-Like Phenotype and Metastatic Burden in ER+HER2+ Mammary Carcinoma,” Cell Death & Disease 16, no. 1 (2025): 76.

[336] S. Chen, X. Zhang, B. Basappa, T. Zhu, V. Pandey, P. E. Lobie, “TFF3 facilitates Dormancy of Anti-estrogen Treated ER+ Mammary Carcinoma,” Communications Medicine (London) 5 (2025): 45.

[337] S. Zhang, Y. Q. Tan, X. Zhang, et al., “TFF3 drives Hippo Dependent EGFR-TKI Resistance in Lung Adenocarcinoma,” Oncogene 44, no. 11 (2025): 753-768.

[338] P. Huang, T. Wolde, V. Bhardwaj, X. Zhang, V. Pandey, “TFF3 and PVRL2 co-targeting Identified by multi-omics Approach as an Effective Cancer Immunosuppression Strategy,” Life Sciences 357 (2024): 123113.

[339] M. Arbitrio, M. Milano, M. Lucibello, et al., “Bioinformatic Challenges for Pharmacogenomic Study: Tools for Genomic Data Analysis,” Frontiers in Pharmacology 16 (2025): 1548991.

[340] Y. Luo, C. Zhao, F. Chen, “Multiomics Research: Principles and Challenges in Integrated Analysis,” BioDesign Research 6 (2024): 0059.

[341] X. Yang, K. Huang, D. Yang, W. Zhao, X. Zhou, “Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review,” Global Challenges 8, no. 1 (2024): 2300163.

[342] E. Pérez-Wohlfeil, O. Torreno, L. J. Bellis, P. L. Fernandes, B. Leskosek, O. Trelles, “Training Bioinformaticians in High Performance Computing,” Heliyon 4, no. 12 (2018): e01057.

[343] J. Rahnenführer, R. De Bin, A. Benner, et al., “Statistical Analysis of High-dimensional Biomedical Data: A Gentle Introduction to Analytical Goals, Common Approaches and Challenges,” BMC Medicine 21, no. 1 (2023): 182.

[344] T. C. Dakal, R. Dhakar, A. Beura, et al., “Emerging Methods and Techniques for Cancer Biomarker Discovery,” Pathology—Research and Practice 262 (2024): 155567.

[345] G. Gómez-López, J. Dopazo, J. C. Cigudosa, A. Valencia, F. Al-Shahrour, “Precision Medicine Needs Pioneering Clinical Bioinformaticians,” Brief Bioinform 20, no. 3 (2019): 752-766.

[346] M. Khalifa, M. Albadawy, “Artificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions,” Computer Methods and Programs in Biomedicine Update 5 (2024): 100148.

[347] M. Martínez-García, E. Hernández-Lemus, “Data Integration Challenges for Machine Learning in Precision Medicine,” Frontiers in Medicine (Lausanne) 8 (2022): 784455.

[348] G. D. Giebel, P. Raszke, H. Nowak, et al., “Problems and Barriers Related to the Use of AI-Based Clinical Decision Support Systems: Interview Study,” Journal of Medical Internet Research 27, no. 1 (2025): e63377.

[349] F. Mirakhori, S. K. Niazi, “Harnessing the AI/ML in Drug and Biological Products Discovery and Development: The Regulatory Perspective,” Pharmaceuticals (Basel) 18, no. 1 (2025): 47.

[350] D. D. Farhud, S. Zokaei, “Ethical Issues of Artificial Intelligence in Medicine and Healthcare,” Iranian Journal of Public Health 50, no. 11 (2021): i-v.

[351] M. Dara, N. Azarpira, “Ethical Considerations Emerge From Artificial Intelligence (AI) in Biotechnology,” Iranian Journal of Public Health 17, no. 1 (2025): 80-81.

[352] S. Wang, X. Jiang, S. Singh, et al., “Genome Privacy: Challenges, Technical Approaches to Mitigate Risk, and Ethical Considerations in the United States,” Annals of the New York Academy of Sciences 1387, no. 1 (2017): 73-83.

[353] R. Chevrier, V. Foufi, C. Gaudet-Blavignac, A. Robert, C. Lovis, “Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review,” Journal of Medical Internet Research [Electronic Resource] 21, no. 5 (2019): e13484.

[354] F. J. Jaime, A. Muñoz, F. Rodríguez-Gómez, A. Jerez-Calero, “Strengthening Privacy and Data Security in Biomedical Microelectromechanical Systems by IoT Communication Security and Protection in Smart Healthcare,” Sensors (Basel) 23, no. 21 (2023): 8944.

[355] P. F. Edemekong, P. Annamaraju, M. Afzal, M. J. Haydel, “Health Insurance Portability and Accountability Act (HIPAA) Compliance,” StatPearls (StatPearls Publishing, 2025). Accessed May 3, 2025. http://www.ncbi.nlm.nih.gov/books/NBK500019/

[356] L. M. Beskow, E. Dean, “Informed Consent for Biorepositories: Assessing Prospective Participants' understanding and Opinions,” Cancer Epidemiology and Prevention Biomarkers 17, no. 6 (2008): 1440-1451.

[357] C. Grady, L. Eckstein, B. Berkman, et al., “Broad Consent for Research with Biological Samples: Workshop Conclusions,” The American Journal of Bioethics 15, no. 9 (2015): 34-42.

[358] D. T. Stein, S. F. Terry, “Reforming Biobank Consent Policy: A Necessary Move away From Broad Consent Toward Dynamic Consent,” Genetic Testing and Molecular Biomarkers 17, no. 12 (2013): 855-856.

[359] D. Horgan, M. Tanner, C. Aggarwal, et al., “Precision Oncology: A Global Perspective on Implementation and Policy Development,” JCO Global Oncology 11 (2025): e2400416. Published online January 2025.

[360] N. Khalid, A. Qayyum, M. Bilal, A. Al-Fuqaha, J. Qadir, “Privacy-preserving Artificial Intelligence in Healthcare: Techniques and Applications,” Computers in Biology and Medicine 158 (2023): 106848.

[361] K. M. Wong, K. Langlais, G. S. Tobias, et al., “The dbGaP Data Browser: A New Tool for Browsing dbGaP Controlled-access Genomic Data,” Nucleic Acids Research 45, no. D1 (2017): D819-D826.

[362] M. B. Davis, R. Martini, “Precision Oncology and Genetic Ancestry: The Science Behind Population-based Cancer Disparities,” Cancer Cell 43, no. 4 (2025): 619-622.

[363] M. Harishbhai Tilala, P. Kumar Chenchala, A. Choppadandi, et al., “Ethical Considerations in the Use of Artificial Intelligence and Machine Learning in Health Care: A Comprehensive Review,” Cureus 16, no. 6: e62443.

[364] J. W. Anderson, S. Visweswaran, “Algorithmic Individual Fairness and Healthcare: A Scoping Review,” JAMIA Open 8, no. 1 (2025): ooae149.

[365] J. Yates, E. M. Van Allen, “New Horizons at the Interface of Artificial Intelligence and Translational Cancer Research,” Cancer Cell 43, no. 4 (2025): 708-727.

[366] P. Moreno, N. Huang, J. R. Manning, et al., “User-friendly, Scalable Tools and Workflows for Single-cell RNAseq Analysis,” Nature Methods 18, no. 4 (2021): 327-328.

[367] K. B. Johnson, W. Q. Wei, D. Weeraratne, et al., “Precision Medicine, AI, and the Future of Personalized Health Care,” Clinical and Translational Science 14, no. 1 (2021): 86-93.

[368] K. S. Kumar, V. Miskovic, A. Blasiak, et al., “Artificial Intelligence in Clinical Oncology: From Data to Digital Pathology and Treatment,” American Society of Clinical Oncology Educational Book 43 (2023): e390084. Published online May 2023.

[369] A. Thorogood, H. L. Rehm, P. Goodhand, et al., “International federation of Genomic Medicine Databases Using GA4GH Standards,” Cell Genomics 1, no. 2 (2021): 100032.

[370] H. L. Rehm, A. J. H. Page, L. Smith, et al., “GA4GH: International Policies and Standards for Data Sharing Across Genomic Research and Healthcare,” Cell Genomics 1, no. 2 (2021): 100029.

[371] Z. Zhang, H. Li, S. Jiang, et al., “A Survey and Evaluation of Web-based Tools/Databases for Variant Analysis of TCGA Data,” Brief Bioinform 20, no. 4 (2019): 1524-1541.

[372] T. Züllig, M. Trötzmüller, H. C. Köfeler, “Lipidomics From Sample Preparation to Data Analysis: A Primer,” Analytical and Bioanalytical Chemistry 412, no. 10 (2020): 2191-2209.

[373] J. A. Tzec-Interián, D. González-Padilla, E. B. Góngora-Castillo, “Bioinformatics Perspectives on Transcriptomics: A Comprehensive Review of Bulk and Single-cell RNA Sequencing Analyses,” Quantitative Biology 13 (2025): e78.

[374] N. Shaban, D. Kamashev, A. Emelianova, A. Buzdin, “Targeted Inhibitors of EGFR: Structure, Biology, Biomarkers, and Clinical Applications,” Cells 13, no. 1 (2023): 47.

[375] S. A. Javed, A. Najmi, W. Ahsan, K. Zoghebi, “Targeting PD-1/PD-L-1 Immune Checkpoint Inhibition for Cancer Immunotherapy: Success and Challenges,” Frontiers in Immunology 15 (2024): 1383456.

[376] J. Zhao, Z. Zhou, P. E. Saw, E. Song, “Silver Jubilee of HER2 Targeting: A Clinical Success in Breast Cancer,” Journal of the National Cancer Center (2025). Published online February 12, https://doi.org/10.1016/j.jncc.2024.12.008.

[377] J. Chen, A. Lin, A. Jiang, et al., “Computational Frameworks Transform Antagonism to Synergy in Optimizing Combination Therapies,” npj Digital Medicine 8 (2025): 44.

[378] S. M. Gadgeel, D. Rodríguez-Abreu, B. Halmos, et al., “Pembrolizumab plus Chemotherapy for Metastatic NSCLC with Programmed Cell Death Ligand 1 Tumor Proportion Score Less than 1%: Pooled Analysis of Outcomes after Five Years of Follow-Up,” Journal of Thoracic Oncology 19, no. 8 (2024): 1228-1241.

[379] J. J. Maly, E. R. Macrae, “Pertuzumab in Combination With Trastuzumab and Chemotherapy in the Treatment of HER2-Positive Metastatic Breast Cancer: Safety, Efficacy, and Progression Free Survival,” Breast Cancer (Auckl) 8 (2014): 81-88.

[380] L. Xiong, Y. Lou, L. Wang, “Effect of bevacizumab Combined With First-line Chemotherapy on Metastatic Colorectal Cancer,” American Journal of Translational Research 13, no. 4 (2021): 3609-3617. Accessed May 3, 2025. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8129318/

[381] M. S. Hassan Zafar, A. A. Khan, S. Aggarwal, M. Bhargava, “Efficacy and Tolerability of Bortezomib and Dexamethasone in Newly Diagnosed Multiple Myeloma,” South Asian Journal of Cancer 7, no. 1 (2018): 58-60.

[382] H. Jamialahmadi, G. Khalili-Tanha, E. Nazari, M. Rezaei-Tavirani, “Artificial Intelligence and Bioinformatics: A Journey From Traditional Techniques to Smart Approaches,” Gastroenterol Hepatol Bed Bench 17, no. 3 (2024): 241-252.

[383] W. Jiang, W. Ye, X. Tan, Y. J. Bao, “Network-based Multi-omics Integrative Analysis Methods in Drug Discovery: A Systematic Review,” BioData Mining 18 (2025): 27.

[384] S. Kulkarni, J. Brownlie, J. N. Jeyapalan, N. P. Mongan, E. A. Rakha, S. Madhusudan, “Evolving DNA Repair Synthetic Lethality Targets in Cancer,” Bioscience Reports 42, no. 12 (2022): BSR20221713.

[385] A. Guan, C. Quek, “Single-Cell Multi-Omics: Insights Into Therapeutic Innovations to Advance Treatment in Cancer,” International Journal of Molecular Sciences 26, no. 6 (2025): 2447.

[386] S. Fang, B. Chen, Y. Zhang, et al., “Computational Approaches and Challenges in Spatial Transcriptomics,” Genomics, Proteomics & Bioinformatics 21, no. 1 (2023): 24-47.

[387] G. Molla Desta, A. G. Birhanu, “Advancements in Single-cell RNA Sequencing and Spatial Transcriptomics: Transforming Biomedical Research,” Acta Biochimica Polonica 72 (2025): 13922.

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