DeepCORE: An interpretable multi-view deep neural network model to detect co-operative regulatory elements
Gene transcription is an essential process involved in all aspects of cellular functions with significant impact on biological traits and diseases. This process is tightly regulated by multiple elements that co-operate to jointly modulate the transcription levels of target genes. To decipher the complicated regulatory network, we present a novel multi-view attention-based deep neural network that models the relationship between genetic, epigenetic, and transcriptional patterns and identifies co-operative regulatory elements (COREs). We applied this new method, named DeepCORE, to predict transcriptomes in 25 different cell lines, which outperformed the state-of-the-art algorithms. Furthermore, DeepCORE translates the attention values embedded in the neural network into interpretable information, including locations of putative regulatory elements and their correlations, which collectively implies COREs. These COREs are significantly enriched with known promoters and enhancers. Novel regulatory elements discovered by DeepCORE showed epigenetic signatures consistent with the status of histone modification marks.
The DNN architecture consists of two separate paths representing the genetic view and the epigenetic view. Each path starts with a CNN layer which is then passed to a ReLU function connected to max pooling. It then uses bi-directional long short-term memory (BiLSTM) networks with attention mechanism to capture the short-range and long-range dependencies. The learnt attention of the two views are concatenated and given to a fully connected network to predict gene transcription levels.
- python 2.7
- tensorflow 1.13.1
- numpy
- scikit-learn
- matplotlib
Copyright (c) 2023 Pramod Bharadwaj Chandrashekar
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