Artificial intelligence applied to omics data in liver diseases: Enhancing clinical predictions.

ABSTRACT: Rapid development of biotechnology has led to the generation of vast amounts of multi-omics data, necessitating the advancement of bioinformatics and artificial intelligence to enable computational modeling to diagnose and predict clinical outcome. Both conventional machine learning and new deep learning algorithms screen existing data unbiasedly to uncover patterns and create models that can be valuable in informing clinical decisions. We summarized published literature on the use of AI models trained on omics datasets, with and without clinical data, to diagnose, risk-stratify, and predict survivability of patients with non-malignant liver diseases. A total of 20 different models were tested in selected studies. Generally, the addition of omics data to regular clinical parameters or indi

SUBMITTER: Baciu C 

PROVIDER: S-EPMC9705954 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature