Revolutionizing multi-omics analysis with artificial intelligence and data processing

Abstract

Our understanding of intricate biological systems has been completely transformed by the development of multi-omics approaches, which entail the simultaneous study of several different molecular data types. However, there are many obstacles to overcome when analyzing multi-omics data, including the requirement for sophisticated data processing and analysis tools. The integration of multi-omics research with artificial intelligence (AI) has the potential to fundamentally alter our understanding of biological systems. AI has emerged as an effective tool for evaluating complicated data sets. The application of AI and data processing techniques in multi-omics analysis is explored in this study. The present study articulates the diverse categories of information generated by multi-omics methodologies and the intricacies involved in managing and merging these datasets. Additionally, it looks at the various AI techniques—such as machine learning, deep learning, and neural networks—that have been created for multi-omics analysis. The assessment comes to the conclusion that multi-omics analysis has a lot of potential to change with the integration of AI and data processing techniques. AI can speed up the discovery of new biomarkers and therapeutic targets as well as the advancement of personalized medicine strategies by enabling the integration and analysis of massive and complicated data sets. The necessity for high-quality data sets and the creation of useful algorithms and models are some of the difficulties that come with using AI in multi-omics study. In order to fully exploit the promise of AI in multi-omics analysis, more study in this area is required.

Keywords

artificial intelligence / data processing / deep learning / machine learning / multi-omics / neural networks

References

[1] Turanli B , Karagoz K , Gulfidan G , Sinha R , Mardinoglu A , Arga KY . A network-based cancer drug discovery: from integrated multi-omics approaches to precision medicine. Curr Pharm Des. 2018; 24 (32): 3778- 90.

[2] Ohashi H , Hasegawa M , Wakimoto K , Miyamoto-Sato E . Next-generation technologies for multiomics approaches including interactome sequencing. BioMed Res Int. 2015; 2015 (1): 104209.

[3] Xu C , Zhang JG , Lin D , Zhang L , Shen H , Deng HW . A systemic analysis of transcriptomic and epigenomic data to reveal regulation patterns for complex disease. G3 (Bethesda). 2017; 7 (7): 2271- 9.

[4] Bansal H , Luthra H , Raghuram SR . A review on machine learning aided multi-omics data integration techniques for healthcare. Data Analy Comput Intell Novel Models Algorithm Appl. 2023: 211- 39.

[5] Coello CEA , Alimam MN , Kouatly R . Effectiveness of ChatGPT in coding: a comparative analysis of popular large language models. Digital. 2024; 4 (1): 114- 25.

[6] Mukherjee A , Abraham S , Singh A , Balaji S , Mukunthan KS . From data to cure: a comprehensive exploration of multi-omics data analysis for targeted therapies. Mol Biotechnol. 2024: 1- 21.

[7] Essien A , Bukoye OT , O'Dea X , Kremantzis M . The influence of AI text generators on critical thinking skills in UK business schools. Stud High Educ. 2024; 49 (5): 865- 82.

[8] Nam Y , Kim J , Jung SH , Woerner J , Suh EH , Lee DG , et al. Harnessing artificial intelligence in multimodal omics data integration: paving the path for the next frontier in precision medicine. Annu Rev Biomed Data Sci. 2024; 7 (1): 225- 50.

[9] Lovino M , Randazzo V , Ciravegna G , Barbiero P , Ficarra E , Cirrincione G . A survey on data integration for multi-omics sample clustering. Neurocomputing. 2022; 488: 494- 508.

[10] Wang D , Gu J . Integrative clustering methods of multi-omics data for molecule-based cancer classifications. Quant Biol. 2016; 4 (1): 58- 67.

[11] Li Y , Mansmann U , Du S , Hornung R . Benchmark study of feature selection strategies for multi-omics data. BMC Bioinf. 2022; 23 (1): 1- 18.

[12] Grzesik P , Augustyn DR , Wyciślik Ł , Mrozek D . Serverless computing in omics data analysis and integration. Briefings Bioinf. 2022; 23 (1): bbab349.

[13] Pinu FR , Beale DJ , Paten AM , Kouremenos K , Swarup S , Schirra HJ , et al. Systems biology and multi-omics integration: viewpoints from the metabolomics research community. Metabolites. 2019; 9 (4): 76.

[14] Krassowski M , Das V , Sahu SK , Misra BB . State of the field in multi-omics research: from computational needs to data mining and sharing. Front Genet. 2020; 11: 610798.

[15] Luan H , Ji F , Chen Y , Cai Z . statTarget: a streamlined tool for signal drift correction and interpretations of quantitative mass spectrometry-based omics data. Anal Chim Acta. 2018; 1036: 66- 72.

[16] Flores JE , Claborne DM , Weller ZD , Webb-Robertson BJM , Waters KM , Bramer LM . Missing data in multi-omics integration: recent advances through artificial intelligence. Front Artif Intell. 2023; 6: 1098308.

[17] Menyhárt O , Győrffy B . Multi-omics approaches in cancer research with applications in tumor subtyping, prognosis, and diagnosis. Comput Struct Biotechnol J. 2021; 19: 949- 60.

[18] Biswas N , Chakrabarti S . Artificial intelligence (AI)-based systems biology approaches in multi-omics data analysis of cancer. Front Oncol. 2020; 10: 588221.

[19] Holzinger A , Haibe-Kains B , Jurisica I . Why imaging data alone is not enough: AI-based integration of imaging, omics, and clinical data. Eur J Nucl Med Mol Imag. 2019; 46 (13): 2722- 30.

[20] Koleini F , Gemperline P , Tabrizi N . Biomarker discovery in multi-omics datasets using tensor decompositions; a comprehensive review. Proc Int Conf Bioinfor. 2023; 92: 11- 24.

[21] Kamya P , Ozerov IV , Pun FW , Tretina K , Fokina T , Chen S , et al. PandaOmics: an AI-driven platform for therapeutic target and biomarker discovery. J Chem Inf Model. 2024; 64 (10): 3961- 9.

[22] Tran TTV , Surya Wibowo A , Tayara H , Chong KT . Artificial intelligence in drug toxicity prediction: recent advances, challenges, and future perspectives. J Chem Inf Model. 2023; 63 (9): 2628- 43.

[23] Singh AV , Chandrasekar V , Paudel N , Laux P , Luch A , Gemmati D , et al. Integrative toxicogenomics: advancing precision medicine and toxicology through artificial intelligence and OMICs technology. Biomed Pharmacother. 2023; 163: 114784.

[24] Zhang B , Kuster B . Proteomics is not an island: multi-omics integration is the key to understanding biological systems. Mol Cell Proteomics. 2019; 18 (8): S1- 4.

[25] Graw S , Chappell K , Washam CL , Gies A , Bird J , Robeson MS , et al. Multi-omics data integration considerations and study design for biological systems and disease. Mol Omics. 2021; 17 (2): 170- 85.

[26] Devyatkin VA , Shklyar AA , Fursova AZ , Rumyantseva YV , Kozhevnikova OS . Allele-specific PCR with fluorescently labeled probes: criteria for selecting primers for genotyping. Vavilov J Genet Breed. 2024; 28 (3): 351- 9.

[27] Ferguson LR , Chen H , Collins AR , Connell M , Damia G , Dasgupta S , et al. Genomic instability in human cancer: molecular insights and opportunities for therapeutic attack and prevention through diet and nutrition. Semin Cancer Biol. 2015; 35: S5- 24.

[28] Xiong Q , Ancona N , Hauser ER , Mukherjee S , Furey TS . Integrating genetic and gene expression evidence into genome-wide association analysis of gene sets. Genome Res. 2012; 22 (2): 386- 97.

[29] Falque M , Jebreen K , Paux E , Knaak C , Mezmouk S , Martin OC . CNVmap: a method and software to detect and map copy number variants from segregation data. Genetics. 2020; 214 (3): 561- 76.

[30] Schmidt B , Hildebrandt A . Next-generation sequencing: big data meets high performance computing. Drug Discov Today. 2017; 22 (4): 712- 7.

[31] Varsamis GD , Karafyllidis IG , Gilkes KM , Arranz U , Martin-Cuevas R , Calleja G , et al. Quantum gate algorithm for reference-guided DNA sequence alignment. Comput Biol Chem. 2023; 107: 107959.

[32] Khatoon Z , Figler B , Zhang H , Cheng F . Introduction to RNA-Seq and its applications to drug discovery and development. Drug Dev Res. 2014; 75 (5): 324- 30.

[33] Tihagam RD , Bhatnagar S . A multi-platform normalization method for meta-analysis of gene expression data. Methods. 2023; 217: 43- 8.

[34] Pertea M , Kim D , Pertea GM , Leek JT , Salzberg SL . Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown. Nat Protoc. 2016; 11 (9): 1650- 67.

[35] Liu Y , Yao M , Li S , Wei X , Ding L , Han S , et al. Integrated application of multi-omics approach and biochemical assays provides insights into physiological responses to saline-alkaline stress in the gills of crucian carp (Carassius auratus). Sci Total Environ. 2022; 822: 153622.

[36] Raj T , Li YI , Wong G , Humphrey J , Wang M , Ramdhani S , et al. Integrative transcriptome analyses of the aging brain implicate altered splicing in Alzheimer's disease susceptibility. Nat Genet. 2018; 50 (11): 1584- 92.

[37] Yan J , Risacher SL , Shen L , Saykin AJ . Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Briefings Bioinf. 2018; 19 (6): 1370- 81.

[38] Wu T , Xiao CL , Lam TTY , Yu G . Biomedical data visualization: methods and applications. Front Genet. 2022; 13: 890775.

[39] Pelletier AR , Chung YE , Ning Z , Wong N , Figeys D , Lavallée-Adam M . MealTime-MS: a machine learning-guided real-time mass spectrometry analysis for protein identification and efficient dynamic exclusion. J Am Soc Mass Spectrom. 2020; 31 (7): 1459- 72.

[40] Ran B . Research progress on tumor protein biomarkers using high-throughput proteomics based on mass spectrometry. Chin J Clin Oncol. 2020: 411- 7.

[41] Pinter N , Glätzer D , Fahrner M , Fröhlich K , Johnson J , Grüning BA , et al. MaxQuant and MSstats in galaxy enable reproducible cloud-based analysis of quantitative proteomics experiments for everyone. J Proteome Res. 2022; 21 (6): 1558- 65.

[42] Schmidt C , Urlaub H . Combining cryo-electron microscopy (cryo-EM) and cross-linking mass spectrometry (CX-MS) for structural elucidation of large protein assemblies. Curr Opin Struct Biol. 2017; 46: 157- 68.

[43] Liang X , Martyniuk CJ , Simmons DB . Are we forgetting the “proteomics” in multi-omics ecotoxicology? Comp Biochem Physiol Genom Proteonomics. 2020; 36: 100751.

[44] Hashiguchi A , Komatsu S . Impact of post-translational modifications of crop proteins under abiotic stress. Proteomes. 2016; 4 (4): 42.

[45] Letertre MP , Dervilly G , Giraudeau P . Combined nuclear magnetic resonance spectroscopy and mass spectrometry approaches for metabolomics. Anal Chem. 2020; 93 (1): 500- 18.

[46] Mamas M , Dunn WB , Neyses L , Goodacre R . The role of metabolites and metabolomics in clinically applicable biomarkers of disease. Arch Toxicol. 2011; 85 (1): 5- 17.

[47] Zhang LJ , Qian L , Ding LY , Wang L , Wong MH , Tao HC . Ecological and toxicological assessments of anthropogenic contaminants based on environmental metabolomics. Environ Sci Ecotechnol. 2021; 5: 100081.

[48] Kang M , Gao J . Integration of multi-omics data for expression quantitative trait loci (eQTL) analysis and eQTL epistasis. eQTL Anal Methods Protoc. 2020: 157- 71.

[49] Wang X , Bajpai AK , Gu Q , Ashbrook DG , Starlard-Davenport A , Lu L . Weighted gene co-expression network analysis identifies key hub genes and pathways in acute myeloid leukemia. Front Genet. 2023; 14: 1009462.

[50] Deng L , Tang Y , Zhang X , Chen J . Structure-adaptive canonical correlation analysis for microbiome multi-omics data. Front Genet. 2024; 15: 1489694.

[51] Argelaguet R , Velten B , Arnol D , Dietrich S , Zenz T , Marioni JC , et al. Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets. Mol Syst Biol. 2018; 14 (6): e8124.

[52] Martens L . Bringing proteomics into the clinic: the need for the field to finally take itself seriously. Proteonomics Clin Appl. 2013; 7 (5-6): 388- 91.

[53] Vaudel M , Verheggen K , Csordas A , Ræder H , Berven FS , Martens L , et al. Exploring the potential of public proteomics data. Proteomics. 2016; 16 (2): 214- 25.

[54] Camacho DM , Collins KM , Powers RK , Costello JC , Collins JJ . Next-generation machine learning for biological networks. Cell. 2018; 173 (7): 1581- 92.

[55] Ali M , Aittokallio T . Machine learning and feature selection for drug response prediction in precision oncology applications. Biophys Rev. 2019; 11 (1): 31- 9.

[56] Cai Z , Poulos RC , Liu J , Zhong Q . Machine learning for multi-omics data integration in cancer. iScience. 2022; 25 (2): 103798.

[57] Huang C , Kuan PF . intCC: an efficient weighted integrative consensus clustering of multimodal data. Pac Symp Biocomput. 2024; 29: 627- 40.

[58] Ayesha S , Hanif MK , Talib R . Overview and comparative study of dimensionality reduction techniques for high dimensional data. Inf Fusion. 2020; 59: 44- 58.

[59] Lin E , Tsai SJ . Multi-omics and machine learning applications in precision medicine. Curr Pharmacogenomics Personalized Med (CPPM). 2017; 15 (2): 97- 104.

[60] Cava C , Sabetian S , Salvatore C , Castiglioni I . Pan-cancer classification of multi-omics data based on machine learning models. Network Model Anal Health Informatics Bioinf. 2024; 13 (1): 6.

[61] Picard M , Scott-Boyer MP , Bodein A , Périn O , Droit A . Integration strategies of multi-omics data for machine learning analysis. Comput Struct Biotechnol J. 2021; 19: 3735- 46.

[62] Bian Y , Xie XQ . Generative chemistry: drug discovery with deep learning generative models. J Mol Model. 2021; 27 (3): 1- 18.

[63] Mendez KM , Broadhurst DI , Reinke SN . The application of artificial neural networks in metabolomics: a historical perspective. Metabolomics. 2019; 15 (11): 1- 14.

[64] Joyce AR , Palsson BØ . The model organism as a system: integrating ‘omics' data sets. Nat Rev Mol Cell Biol. 2006; 7 (3): 198- 210.

[65] Lee M . Recent advances in generative adversarial networks for gene expression data: a comprehensive review. Mathematics. 2023; 11 (14): 3055.

[66] Khosravi A , Nahavandi S , Creighton D , Atiya AF . Lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans Neural Network. 2010; 22 (3): 337- 46.

[67] Wang K , Abid MA , Rasheed A , Crossa J , Hearne S , Li H . DNNGP, a deep neural network-based method for genomic prediction using multi-omics data in plants. Mol Plant. 2023; 16 (1): 279- 93.

[68] Shameer K , Johnson KW , Glicksberg BS , Dudley JT , Sengupta PP . Machine learning in cardiovascular medicine: are we there yet? Heart. 2018; 104 (14): 1156- 64.

[69] Narayanan R , DeGroat W , Mendhe D , Abdelhalim H , Ahmed Z . IntelliGenes: interactive and user-friendly multimodal AI/ML application for biomarker discovery and predictive medicine. Biol Methods Protoc. 2024; 9 (1): bpae040.

[70] Su TH , Wu CH , Kao JH . Artificial intelligence in precision medicine in hepatology. J Gastroenterol Hepatol. 2021; 36 (3): 569- 80.

[71] Tsai CW , Lai CF , Chao HC , Vasilakos AV . Big data analytics: a survey. J Big Data. 2015; 2 (1): 1- 32.

[72] Reska D , Czajkowski M , Jurczuk K , Boldak C , Kwedlo W , Bauer W , et al. Integration of solutions and services for multi-omics data analysis towards personalized medicine. Biocybern Biomed Eng. 2021; 41 (4): 1646- 63.

[73] Kalogirou SA . Applications of artificial neural-networks for energy systems. Appl Energy. 2000; 67 (1-2): 17- 35.

[74] Vandereyken K , Sifrim A , Thienpont B , Voet T . Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet. 2023; 24 (8): 1- 22.

[75] Mohr AE , Ortega-Santos CP , Whisner CM , Klein-Seetharaman J , Jasbi P . Navigating challenges and opportunities in multi-omics integration for personalized healthcare. Biomedicines. 2024; 12 (7): 1496.

[76] Al-Tashi Q , Saad MB , Muneer A , Qureshi R , Mirjalili S , Sheshadri A , et al. Machine learning models for the identification of prognostic and predictive cancer biomarkers: a systematic review. Int J Mol Sci. 2023; 24 (9): 7781.

[77] Ahn JS , Shin S , Yang SA , Park EK , Kim KH , Cho SI , et al. Artificial intelligence in breast cancer diagnosis and personalized medicine. J Breast Cancer. 2023; 26 (5): 405.

[78] Momeni Z , Hassanzadeh E , Abadeh MS , Bellazzi R . A survey on single and multi omics data mining methods in cancer data classification. J Biomed Inf. 2020; 107: 103466.

[79] Ahmed Z . Practicing precision medicine with intelligently integrative clinical and multi-omics data analysis. Hum Genom. 2020; 14 (1): 35.

[80] Kudryashova KS , Burka K , Kulaga AY , Vorobyeva NS , Kennedy BK . Aging biomarkers: from functional tests to multi-omics approaches. Proteomics. 2020; 20 (5-6): 1900408.

[81] Tolios A , De Las Rivas J , Hovig E , Trouillas P , Scorilas A , Mohr T . Computational approaches in cancer multidrug resistance research: identification of potential biomarkers, drug targets and drug-target interactions. Drug Resist Updates. 2020; 48: 100662.

[82] Hao J , Masum M , Oh JH , Kang M . Gene- and pathway-based deep neural network for multi-omics data integration to predict cancer survival outcomes. In: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics); 2019;

[83] Valous NA , Popp F , Zörnig I , Jäger D , Charoentong P . Graph machine learning for integrated multi-omics analysis. Br J Cancer. 2024; 131: 205- 11.

[84] Luo J , Wu M , Gopukumar D , Zhao Y . Big data application in biomedical research and health care: a literature review. Biomed Inf Insights. 2016; 8: BII- S31559.

[85] Lei Y , Guo J , He S , Yan H . Essential role of multi-omics approaches in the study of retinal vascular diseases. Cells. 2023; 12 (1): 103.

[86] Patel SK , George B , Rai V . Artificial intelligence to decode cancer mechanism: beyond patient stratification for precision oncology. Front Pharmacol. 2020; 11: 1177.

[87] Danishuddin , Khan S , Kim JJ . From cancer big data to treatment: artificial intelligence in cancer research. J Gene Med. 2024; 26 (1): e3629.

[88] Rivero-Segura NA , Bello-Chavolla OY , Barrera-Vázquez OS , Gutierrez-Robledo LM , Gomez-Verjan JC . Promising biomarkers of human aging: in search of a multi-omics panel to understand the aging process from a multidimensional perspective. Ageing Res Rev. 2020; 64: 101164.

[89] Eccles SA , Aboagye EO , Ali S , Anderson AS , Armes J , Berditchevski F , et al. Critical research gaps and translational priorities for the successful prevention and treatment of breast cancer. Breast Cancer Res. 2013; 15 (5): 1- 37.

[90] Kartha VK , Duarte FM , Hu Y , Ma S , Chew JG , Lareau CA , et al. Functional inference of gene regulation using single-cell multi-omics. Cell Genom. 2022; 2 (9): 100166.

[91] Janiesch C , Zschech P , Heinrich K . Machine learning and deep learning. Electron Mark. 2021; 31 (3): 685- 95.

[92] Sidak D , Schwarzerová J , Weckwerth W , Waldherr S . Interpretable machine learning methods for predictions in systems biology from omics data. Front Mol Biosci. 2022; 9: 926623.

[93] Oldoni E , Saunders G , Bietrix F , Garcia Bermejo ML , Niehues A , 't Hoen PA , et al. Tackling the translational challenges of multi-omics research in the realm of European personalised medicine: a workshop report. Front Mol Biosci. 2022; 9: 974799.

[94] Holzinger A , Dehmer M , Emmert-Streib F , Cucchiara R , Augenstein I , Del Ser J , et al. Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence. Inf Fusion. 2022; 79: 263- 78.

[95] Moreta-Moraleda C , Queralt C , Vendrell-Ayats C , Forcales S , Martínez-Balibrea E . Chromatin factors: ready to roll as biomarkers in metastatic colorectal cancer? Pharmacol Res. 2023; 196: 106924.

[96] Liang A , Kong Y , Chen Z , Qiu Y , Wu Y , Zhu X , et al. Advancements and applications of single-cell multi-omics techniques in cancer research: unveiling heterogeneity and paving the way for precision therapeutics. Biochem Biophysics Rep. 2024; 37: 101589.

[97] Ahmed Z . Precision medicine with multi-omics strategies, deep phenotyping, and predictive analysis. Prog Mol Biol Transl Sci. 2022; 190 (1): 101- 25.

RIGHTS & PERMISSIONS

The Author(s). Quantitative Biology published by John Wiley & Sons Australia, Ltd on behalf of Higher Education Press.