Title: GENE SELECTION & HUB GENE IDENTIFICATION USING MULTI OMICS DATA AND MACHINE LEARNING

Speaker Name:  Dr. Tripti Swarnkar

Speaker Email: triptiswarnakar@soa.ac.in

Speaker Affiliation:  Professor, Department of Computer Application, Faculty of Engineering & Technology (ITER), Siksha ‘O’ Anusandhan Deemed to be University (SOADU)

Abstract:

Gene selection or biomarker discovery is the process of identifying a subset of relevant and informative genes from the original set of genes with diagnostic and prognostic capability. Their discriminative ability allows classifying samples into disease categories (diagnostic), while their predictive power enables assessing the cause of disease and discovery of new therapy (prognostic). Even though the DNA microarray/NGS technology has given the researchers, a remarkable opportunity to analyse the expression pattern or genetic signature of thousands of genes concurrently in a solitary platform, still it is limited with large dimension, high noise, batch effect, and low reproducibility. Technological advances in high throughput sequencing technology generate a plethora of data from multiple levels of biological systems such as genome, epigenome, transcriptome, proteome, and metabolome, which is collectively called as “multi-omics” data. Conventional approaches of gene selection mostly rely on analyzing the different types of omics data at a single level that focuses on identifying the variations at a single level, at the same time neglecting the causal relationship between multiple levels of biological entities This talk focuses on outstretching the existing machine learning-based gene selection approaches by acclimatizing network-based gene selection using multiple levels of omics data. The objective here is to perform gene selection and identify few signature hub genes in the genetic network, which are statistically competent and biologically enriched.

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