NOVEL MACHINE LEARNING APPROACH FOR THE IDENTIFICATION OF GENOMIC FEATURES ASSOCIATED WITH EPIGENETIC CONTROL REGIONS AND TRANSGENERATIONAL INHERITANCE OF EPIMUTATIONS

摘要

A two-step (sequential) machine learning analysis tool is provided that involves a combination of an initial active learning step followed by an imbalance class learner (ACL-ICL) protocol. This technique provides a more tightly integrated approach for a more efficient and accurate machine learning analysis. The combination of ACL and ICL work synergistically to improve the accuracy and efficiency of machine learning and can be used with any type of dataset including biological datasets.