Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Schiller International University, Heidelberg Campus, Zollhofgarten 1, 69115 Heidelberg, Germany
Research
DMWAS: Feature Set Optimization by Clustering, Univariate Association, Deep and Machine Learning Omics Wide Association Study for Biomarkers Discovery as Tested on GTEx Pilot Dataset for Death Due to Heart-Attack
Author(s): Abhishek Narain Singh*
Univariate and multivariate methods for association of the genomic variations with the end-or-endo-phenotype
have been widely used for genome wide association studies. In addition to encoding the SNPs, we advocate usage
of clustering as a novel method to en-code the structural variations, SVs, in genomes, such as the deletions and
insertions polymorphism (DIPs), Copy Number Variations (CNVs), translocation, inversion, etc., that can be
used as an independent feature variable value for downstream computation by artificial intelligence methods to
predict the endo-or-end phenotype. We introduce a clustering based encoding scheme for structural variations
and omics based analysis. We conducted a complete all genomic variants association with the phenotype using
deep learning and other machine learning techniques, though other methods such as genetic algorithm can
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