Journal of Proteomics & Bioinformatics
Open Access
ISSN: 0974-276X
ISSN: 0974-276X
Department of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India
Research Article
A Two-Step Approach-Machine Learning, Variational Autoencoder, and Weighted Gene Co-Expression Network Analysis Identify Key Signature Genes and Pathways Implicated in Active Visceral Leishmaniasis
Author(s): Ram Nayan Verma*, Naidu Subbarao and Gajendra Pratap Singh
Leishmania donovani, a kinetoplastid parasite causing leishmaniasis, is an opportunistic parasitic pathogen that affects immunocompromised individuals and is a common cause of Kala-azar. Specific parasite molecules can be delivered into host epithelial cells and may act as effector molecules for intracellular parasite development. So, there is a need to develop new approaches to understanding the interaction between the host and the pathogen. In our study, we built a weighted gene co-expression network using differentially expressed genes obtained through analysis of leishmaniasis-infected patients. Our goal was to identify key signature genes and pathways associated with visceral leishmaniasis infection by network biology analysis which can identify the most influential genes in the gene coexpression interaction network. We identified five prominent genes, IFNG, SC5D, LS.. View More»
DOI:
10.35248/0974-276X.23.16.648