Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 19, Issue 3, Pages 1379-1386Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2021.3099068
Keywords
Bioinformatics; Pipelines; Genomics; Drugs; Biology; Solid modeling; Frequency selective surfaces; Automated machine learning; coronary artery disease; genome-wide association studies; SHAP values
Categories
Funding
- NIH [LM010098]
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Machine Learning approaches, such as TPOT, are being increasingly used in biomedical applications. This study aimed to assess the suitability of TPOT in genomics and identify SNP combinations associated with coronary artery disease (CAD). The results showed a promising approach towards precision medicine.
Machine Learning (ML) approaches are increasingly being used in biomedical applications. Important challenges of ML include choosing the right algorithm and tuning the parameters for optimal performance. Automated ML (AutoML) methods, such as Tree-based Pipeline Optimization Tool (TPOT), have been developed to take some of the guesswork out of ML thus making this technology available to users from more diverse backgrounds. The goals of this study were to assess applicability of TPOT to genomics and to identify combinations of single nucleotide polymorphisms (SNPs) associated with coronary artery disease (CAD), with a focus on genes with high likelihood of being good CAD drug targets. We leveraged public functional genomic resources to group SNPs into biologically meaningful sets to be selected by TPOT. We applied this strategy to data from the U.K. Biobank, detecting a strikingly recurrent signal stemming from a group of 28 SNPs. Importance analysis of these SNPs uncovered functional relevance of the top SNPs to genes whose association with CAD is supported in the literature and other resources. Furthermore, we employed game-theory based metrics to study SNP contributions to individual-level TPOT predictions and discover distinct clusters of well-predicted CAD cases. The latter indicates a promising approach towards precision medicine.
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