Small Molecule Identification and Differentially Expressed Gene Profiling - a Machine Learning Approach for Treating Depression Influenced Cardiomyopathy

Posted: 4 Feb 2020

Date Written: February 1, 2020

Abstract

Cardiomyopathy (CM), a ventricular dilation condition, is a common cause of cardiac failure. Using microarray datasets, linear regression model was constructed to compare the gene expression in CM and control samples for differentially expressed gene (DEG) identification and pathway enrichment of DEGs, hierarchical clustering analysis and geneset analysis tool kit platform were used. GeneSet Enrichment Analysis (GSAE) was performed to identify the potential TFs and miRNAs of DEGs were detected by utilizing hypergeometric distribution. Identification of potential small molecules associated with CM was carried out. The results of this study could lead to the development of lead molecules for preventing CM.

Keywords: Machine learning, Cardiomyopathy, Differentially expressed genes, Pathway enrichment, Protein-Protein Interaction, Transcription factors, Lead identification

Suggested Citation

Padmanabhan, Arul Mozhi and Kuberapandian, Dharaniyambigai and Doss, Victor Arokia, Small Molecule Identification and Differentially Expressed Gene Profiling - a Machine Learning Approach for Treating Depression Influenced Cardiomyopathy (February 1, 2020). Proceedings of International Conference on Drug Discovery (ICDD) 2020. Available at SSRN: https://ssrn.com/abstract=3529897

Arul Mozhi Padmanabhan (Contact Author)

Independent ( email )

No Address Available
United States

Dharaniyambigai Kuberapandian

Independent ( email )

No Address Available
United States

Victor Arokia Doss

Independent ( email )

No Address Available
United States

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