AUTHOR=Biswas Nupur , Chakrabarti Saikat 

TITLE=Artificial Intelligence (AI)-Based Systems Biology Approaches in Multi-Omics Data Analysis of Cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 10 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2020.588221

DOI=10.3389/fonc.2020.588221

ISSN=2234-943X

ABSTRACT=Cancer is the manifestation of abnormalities of different physiological processes involving genes, DNAs, RNAs, proteins, and other bio-molecules whose profiles are reflected in different omics data types. As these bio-entities are very much correlated, integrative analysis of different types of omics data, multi-omics data, is required to understanding the disease from the tumorigenesis to the disease progression. Artificial intelligence (AI), specifically, machine learning algorithms have the ability to make decisive interpretation of ‘big’ sized complex data and, hence, appear as the most effective tools for the analysis, understanding of multi-omics data for patient specific observations. In this review, we have discussed about the recent outcomes of employing artificial intelligence in multi-omics data analysis of different types of cancers. Based on the research trends and significance in patient treatment, we have primarily focused on the AI based analysis for determining cancer subtypes, disease prognosis, and therapeutic targets. We have also discussed about AI analysis of some non-canonical types of omics data as they have the capability of playing determiner role in cancer patient care. Additionally, we have briefly discussed about the data repositories because of their pivotal role in multi-omics data storing, processing and analysis.