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.
@cite_18 leveraged the correlated nature of epigenetic signals in the REMC database, including histone modifications. Their tool, ChromImpute, imputed signals for a particular new sample using an ensemble of regression trees on all the other signals and samples. EFilter ( @cite_28 ), a linear estimation algorithm, predicted gene expression in a new sample by using imputed expression levels from similar samples. Unlike the studies discussed above, these works focus on imputing or predicting signals for new samples.
@cite_28 @cite_18
2049684926 2019251848
Optimized algorithms from the field of electrical-signal processing improve the identification of genomic signals from diverse high-throughput sequencing experiments, such as ChIP-seq, DNase-seq and FAIRE-seq. Large-scale epigenomic profiles are predicted from experimental data using multiple regression tree models.