  Motivation: Histone modifications are among the most important factors that
control gene regulation. Computational methods that predict gene expression
from histone modification signals are highly desirable for understanding their
combinatorial effects in gene regulation. This knowledge can help in developing
'epigenetic drugs' for diseases like cancer. Previous studies for quantifying
the relationship between histone modifications and gene expression levels
either failed to capture combinatorial effects or relied on multiple methods
that separate predictions and combinatorial analysis. This paper develops a
unified discriminative framework using a deep convolutional neural network to
classify gene expression using histone modification data as input. Our system,
called DeepChrome, allows automatic extraction of complex interactions among
important features. To simultaneously visualize the combinatorial interactions
among histone modifications, we propose a novel optimization-based technique
that generates feature pattern maps from the learnt deep model. This provides
an intuitive description of underlying epigenetic mechanisms that regulate
genes. Results: We show that DeepChrome outperforms state-of-the-art models
like Support Vector Machines and Random Forests for gene expression
classification task on 56 different cell-types from REMC database. The output
of our visualization technique not only validates the previous observations but
also allows novel insights about combinatorial interactions among histone
modification marks, some of which have recently been observed by experimental
studies.
