The past decade has seen a revolution in genomic technologies that enabled a flood of genome-wide profiling of chromatin marks. Recent literature tried to understand gene regulation by predicting gene expression from large-scale chromatin measurements. Two fundamental challenges exist for such learning tasks: (1) genome-wide chromatin signals are spatially structured, high-dimensional and highly modular; and (2) the core aim is to understand what are the relevant factors and how they work together. Previous studies either failed to model complex dependencies among input signals or relied on separate feature analysis to explain the decisions. This paper presents an attention-based deep learning approach; AttentiveChrome, that uses a unified architecture to model and to interpret dependencies among chromatin factors for controlling gene regulation. AttentiveChrome uses a hierarchy of multiple Long Short-Term Memory (LSTM) modules to encode the input signals and to model how various chromatin marks cooperate automatically. AttentiveChrome trains two levels of attention jointly with the target prediction, enabling it to attend differentially to relevant marks and to locate important positions per mark. We evaluate the model across 56 different cell types (tasks) in human. Not only is the proposed architecture more accurate, but its attention scores also provide a better interpretation than state-of-the-art feature visualization methods such as saliency map.
Ritambhara Singh (University of Virginia)
Jack Lanchantin (University of Virginia)
Jack Lanchantin is a 4th year PhD student in the department of computer science at the University of Virginia, working with Dr. Yanjun Qi. His interests are primarily in the application of deep learning for biomedical applications.
Arshdeep Sekhon (University of Virginia)
Yanjun Qi (University of Virginia)
More from the Same Authors
2021 : Towards Automatic Actor-Critic Solutions to Continuous Control »
Jake Grigsby · Jin Yong Yoo · Yanjun Qi
2020 : Transfer Learning with Neural Motif Transformer for Predicting Protein-Protein Interactions Between SARS-CoV-2 and Humans »
2017 : Contributed talk: Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks »
Jack Lanchantin · Ritambhara Singh · Beilun Wang
2017 : Poster Spotlights I »
Taesik Na · Yang Song · Aman Sinha · Richard Shin · Qiuyuan Huang · Nina Narodytska · Matt Staib · Kexin Pei · Fnu Suya · Amirata Ghorbani · Jacob Buckman · Matthias Hein · Huan Zhang · Yanjun Qi · Yuan Tian · Min Du · Dimitris Tsipras