A computational model to identify fertility-related proteins using sequence information

Abstract

Fertility is the most crucial step in the development process, which is controlled by many fertility-related proteins, including spermatogenesis-, oogenesis- and embryogenesis-related proteins. The identification of fertility-related proteins can provide important clues for studying the role of these proteins in development. Therefore, in this study, we constructed a two-layer classifier to identify fertility-related proteins. In this classifier, we first used the composition of amino acids (AA) and their physical and chemical properties to code these three fertility-related proteins. Then, the feature set is optimized by analysis of variance (ANOVA) and incremental feature selection (IFS) to obtain the optimal feature subset. Through five-fold cross-validation (CV) and independent data tests, the performance of models constructed by different machine learning (ML) methods is evaluated and compared. Finally, based on support vector machine (SVM), we obtained a two-layer model to classify three fertility-related proteins. On the independent test data set, the accuracy (ACC) and the area under the receiver operating characteristic curve (AUC) of the first layer classifier are 81.95% and 0.89, respectively, and them of the second layer classifier are 84.74% and 0.90, respectively. These results show that the proposed model has stable performance and satisfactory prediction accuracy, and can become a powerful model to identify more fertility related proteins.

Graphical abstract

Keywords

fertility-related proteins / machine learning / sequence information / feature selection

Yan Lin, a professor of Animal Nutrition Institute at Sichuan Agricultural University, China. Her research is in the areas of animal nutrition and feed science

Jiashu Wang, a master candidate of Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, China. His research interests include bioinformatics and machine learning

Xiaowei Liu, a master candidate of Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, China. Her research interests include bioinformatics, machine learning and drug development

Xueqin Xie, a master candidate of Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, China. Her research interests are bioinformatics, machine learning and biomarker prediction

De Wu, a professor of Animal Nutrition Institute at Sichuan Agricultural University, China. His research is in the areas of animal nutrition and feed science

Junjie Zhang, a professor of College of Life Science at Sichuan Agricultural University, China. His research is in the areas of molecular biology of starch synthesis in maize

Hui Ding, an associate professor of Center for Informational Biology at University of Electronic Science and Technology of China, China. Her research is in the areas of computational biology and system biology

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Acknowledgements

This research was funded by the Sichuan Major Science and Technology Project (2021ZDZX0009); the National Natural Science Foundation of China (Grant No. 035Z2060).

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