Computational Algorithms in Biology: Decision Trees, Bayesian Networks, and Beyond

: In the dynamic landscape of computational biology, decision trees and Bayesian networks have become indispensable for tackling complex biological data. This abstract examines their transformative applications in elucidating genetic interactions, predicting disease outcomes, and modelling ecological dynamics. By harnessing the robust capabilities of these algorithms, researchers are able to extract meaningful patterns from vast datasets, catalysing innovations in personalized medicine and biodiversity conservation. Additionally, we highlight emerging methodologies that enhance and expand upon these foundational techniques, paving the way for pioneering discoveries and deepening our understanding of biological systems in an increasingly data-driven world.