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A machine-learning framework for helping to decode the rules by which transcription factors bind their target sites

Posted by An Zheng on behalf of Gymrek Lab

This blog post introduces our paper “Deep neural networks identify sequence context features predictive of transcription factor binding” published on Nature Machine Intelligence [1]. Here is a free read-only copy of this paper [2]. A part of this work was also presented in the 2019 ICML Workshop on Computational Biology [3].

TL;DR We designed a machine learning framework, AgentBind, to identify and interpret sequence features that are most important for transcription factor (TF) binding…

An Zheng

Ph.D. candidate in Computer Science at UC San Diego.

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