Bayesian networks elucidate complex genomic landscapes in cancer.

ABSTRACT: Bayesian networks (BNs) are disciplined, explainable Artificial Intelligence models that can describe structured joint probability spaces. In the context of understanding complex relations between a number of variables in biological settings, they can be constructed from observed data and can provide a guiding, graphical tool in exploring such relations. Here we propose BNs for elucidating the relations between driver events in large cancer genomic datasets. We present a methodology that is specifically tailored to biologists and clinicians as they are the main producers of such datasets. We achieve this by using an optimal BN learning algorithm based on well established likelihood functions and by utilising just two tuning parameters, both of which are easy to set and have intuitive readi

SUBMITTER: Angelopoulos N 

PROVIDER: S-EPMC8980036 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature