Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine

• Published in: Briefings in bioinformatics Vol. 23; no. 5
• Main Authors: Vadapalli, Sreya, Abdelhalim, Habiba, Zeeshan, Saman, Ahmed, Zeeshan
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 20.09.2022; Oxford Publishing Limited (England)
• Subjects: Algorithms; Artificial Intelligence; Comparative analysis; Data sources; DNA microarrays; Gene Expression; Gene sequencing; Genetics; Genomics; Genomics - methods; Learning algorithms; Machine Learning; Precision medicine; Precision Medicine - methods; Review; machine learning; artificial intelligence; gene expression; gene variant; predictive analysis
• DOI: 10.1093/bib/bbac191
• ISSN: 1467-5463; 1477-4054

Abstract Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.