PREDICTING MULTIPLE TYPES OF BIOLOGICAL RELATIONSHIPS WITH INTEGRATIVE NON-NEGATIVE MATRIX FACTORIZATION

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2022-5-09

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KARTLI, Onur Savaş

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Integrative research on multi-modal biological data is difficult due to their complexity and diverse structure. A critical issue in bioinformatics and computational biology is that many of the associations/relationships between biological components and concepts (i.e., genes, proteins, drugs, diseases, etc.) are still unknown due to the high costs and temporal requirements of wet-lab experiments that uncover them. This thesis aims to predict unknown relationships in biological data by leveraging documented protein-protein, drug-target, gene-disease, and drug-side effect associations. To accomplish this task, first, biological datasets are obtained from UniProt, String, Stitch, Sider, Drugbank, Drugcentral, DisGENET, and KEGG databases, and their relationships are extracted and re-formatted as multiple pairwise relationship matrices. Some of these matrices contain continuous values to be used as association weights. We obtain highly sparse matrices mainly due to the high amount of missing data in biological databases. Second, we predicted missing relationships via integrative matrix factorization, using the non-negative matrix tri-factorization algorithm which is shown to successfully solve similar problems in the literature. For this, a prediction model is trained and evaluated using both classification and regression-based metrics. Subsequently, large-scale prediction of pairwise relationships between proteins, drugs, diseases, and side effects is accomplished using the optimized model. We obtained new predictions for drug-side effect, drug-disease, drug-target protein, and gene/protein-disease interactions. We evaluated the top 250 predictions with the highest scores and validated selected ones from the literature. We hope that the results of this thesis study will help life scientists in planning experimental work by providing preliminary sets of biological associations.

Subject Keywords

Non-negative matrix factorization, multi-relational data, drug-target interactions, drug-side effects relationships, gene-disease associations

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https://hdl.handle.net/11511/97348

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Graduate School of Informatics, Thesis

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Citation Formats

O. S. KARTLI, “PREDICTING MULTIPLE TYPES OF BIOLOGICAL RELATIONSHIPS WITH INTEGRATIVE NON-NEGATIVE MATRIX FACTORIZATION,” M.S. - Master of Science, Middle East Technical University, 2022.