Machine Learning Modeling from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease Diagnosis and Clinical Classifications

Abstract

Research of inflammatory bowel disease (IBD) has identified numerous molecular players involved in the disease development. Even so, the understanding of IBD is incomplete, while disease treatment is still far from the precision medicine. Reliable diagnostic and prognostic biomarkers in IBD are limited which may reduce efficient therapeutic outcomes. High-throughput technologies and artificial intelligence emerged as powerful tools in search of unrevealed molecular patterns that could give important insights into IBD pathogenesis and help to address unmet clinical needs. Machine learning, a subtype of artificial intelligence, uses complex mathematical algorithms to learn from existing data in order to predict future outcomes. The scientific community has been increasingly employing machine learning for the prediction of IBD outcomes from comprehensive patient data-clinical records, genomic, transcriptomic, proteomic, metagenomic, and other IBD relevant omics data. This review aims to present fundamental principles behind machine learning modeling and its current application in IBD research with the focus on studies that explored genomic and transcriptomic data. We described different strategies used for dealing with omics data and outlined the best-performing methods. Before being translated into clinical settings, the developed machine learning models should be tested in independent prospective studies as well as randomized controlled trials.

Advancing precision medicine in medical education: Integrated, precise and data‐driven smart solutions

“…Rusek and Krasowska [66] examine noncoding RNA in systemic sclerosis as a valuable tool for translational and personalized medicine. Stankovic et al [67] present fundamental principles behind machine learning modeling and its current application in Inflammatory Bowel Disease research with the focus on studies that explored genomic and transcriptomic data. Termine et al [68] explore the use of Big Data through Artificial Intelligence in Neurodegenerative Diseases.…”

“…At the same time the benefits of Precision/Personalized Medicine and the major obstacles for all stakeholders involved are summarized as well as specific solutions are provided. Knowledge and perception of Medical and Pharmacy students towards Pharmacogenomics and Genetics is also presented [6, 8–10, 22, 27–29, 33, 34, 45, 51, 53–55, 59, 67, 74–77, 81–83, 85–107, 112, 113, 121].…”

Advancing the precision management of inflammatory bowel disease in the era of omics approaches and new technology

“…Machine learning includes supervised and unsupervised algorithms. Supervised algorithms are often used for classification or prediction using example data, while unsupervised algorithms are often used for clustering according to similarity[ 100 ]. These approaches could be well applied to address the need for patient clustering and predictions and the detection of novel biomarkers.…”

Integration and implementation of precision medicine in the multifaceted inflammatory bowel disease

“…According to a study by Stankovic et al [ 58 ], the use of machine learning approaches by using artificial intelligence (AI) and algorithms creates patterns not seen before in the management of IBD[ 58 ]. By creating big datasets and then using linear regressions, logistic regressions, and fitting models to characterize risk factors, patient baseline characteristics, treatment plans, and prognosis can be predefined.…”