Scrutinizing the Disease Based on Omics

EasyChair Preprint 5208

Abstract

Molecular biomarkers are certain molecules or set of molecules that can be of help for diagnosis or prognosis of diseases or disorders. In the past decades, thanks to the advances in high-throughput technologies, a huge amount of molecular ‘omics’ data, e.g. transcriptomics and proteomics, have been accumulated. The availability of these omics data makes it possible to screen bioma rkers for diseases or disorders. Accordingly, a number of computational approaches have been developed to identify biomarkers by exploring the omics data. In this review, we present a comprehensive survey on the recent progress of identification of molecular biomarker s with machine learning approaches. Specifically, we categorize the machine learning approaches into supervised, un-supervised and recommendation approaches, where the biomarkers including single genes, gene sets and small gene networks. In addition, we fu rther discuss potential problems underlying bio-medical data that may pose challenges for machine learning, and provide possible directions for future biomarker identification.

Keyphrases: Disease Diagnosis, Molecular biomarker, gene prioritization, machine learning, precision medicine

@booklet{EasyChair:5208,
  author    = {M Sathish and C Saravanan and G Sethu Pandian and P Indirajith},
  title     = {Scrutinizing the Disease Based on Omics},
  howpublished = {EasyChair Preprint 5208},
  year      = {EasyChair, 2021}}