Highly Scalable Task Grouping for Deep Multi-Task Learning in Prediction of Epigenetic Events.

ABSTRACT: DNNs trained for predicting cellular events from DNA sequence have become emerging tools to help elucidate biological mechanisms underlying associations identified in genome-wide association studies. To enhance the training, multi-task learning (MTL) has been commonly exploited in previous works where trained networks were needed for multiple profiles differing in either event modality or cell type. All existing works adopted a simple MTL framework where all tasks share a single feature extraction network. Such a strategy even though effective to a certain extent leads to substantial negative transfer, meaning the existence of a large portion of tasks for which models obtained through MTL perform worse than those by single-task learning. There have been methods developed to address such ne

SUBMITTER: Shiri M 

PROVIDER: S-EPMC10779439 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature