NetTIME: a multitask and base-pair resolution framework for improved transcription factor binding site prediction.

ABSTRACT:

Motivation

Machine learning models for predicting cell-type-specific transcription factor (TF) binding sites have become increasingly more accurate thanks to the increased availability of next-generation sequencing data and more standardized model evaluation criteria. However, knowledge transfer from data-rich to data-limited TFs and cell types remains crucial for improving TF binding prediction models because available binding labels are highly skewed towards a small collection of TFs and cell types. Transfer prediction of TF binding sites can potentially benefit from a multitask learning approach; however, existing methods typically use shallow single-task models to generate low-resolution predictions. Here, we propose NetTIME, a multitask learning framework for predicting cell-t

SUBMITTER: Yi R 

PROVIDER: S-EPMC9563695 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature