SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model

• URL: http://arxiv.org/abs/2204.02130v2
• Date: Wed, 6 Apr 2022 10:25:29 GMT
• Title: SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model
• Authors: Yikang Zhang, Xiaomin Chu, Yelu Jiang, Hongjie Wu and Lijun Quan
• Abstract summary: We propose a new solution named SemanticCAP to identify accessible regions of the genome. It introduces a gene language model which models the context of gene sequences, thus being able to provide an effective representation of gene sequences. Compared with other systems under public benchmarks, our model proved to have better performance.
• Score: 3.0643865202019698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affects drug-DNA interactions, but also promote or inhibit the expression of critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However, Biological experimental techniques for measuring it are expensive and time consuming. In recent years, several kinds of computational methods have been proposed to identify accessible regions of the genome. Existing computational models mostly ignore the contextual information of bases in gene sequences. To address these issues, we proposed a new solution named SemanticCAP. It introduces a gene language model which models the context of gene sequences, thus being able to provide an effective representation of a certain site in gene sequences. Basically, we merge the features provided by the gene language model into our chromatin accessibility model. During the process, we designed some methods to make feature fusion smoother. Compared with other systems under public benchmarks, our model proved to have better performance.

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