iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation pred...

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Bibliographic Details
Published in:Genome Biology 2022-10, Vol.23 (1), p.219-23, Article 219
Main Authors: Jin, Junru, Yu, Yingying, Wang, Ruheng, Zeng, Xin, Pang, Chao, Jiang, Yi, Li, Zhongshen, Dai, Yutong, Su, Ran, Zou, Quan, Nakai, Kenta, Wei, Leyi
Format: Article
Language:English
Subjects:
Cell cycle
Datasets
Deep learning
DNA Methylation
Epigenetics
Gene mapping
Genomes
Genomics
Interpretable analysis
Language
Learning
Machine learning
Method
Models, Biological
Multi-scale information processing
Natural language processing
Nucleotide sequence
Performance evaluation
Predictions
Semantics
Task analysis
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Summary:In this study, we propose iDNA-ABF, a multi-scale deep biological language learning model that enables the interpretable prediction of DNA methylations based on genomic sequences only. Benchmarking comparisons show that our iDNA-ABF outperforms state-of-the-art methods for different methylation predictions. Importantly, we show the power of deep language learning in capturing both sequential and functional semantics information from background genomes. Moreover, by integrating the interpretable analysis mechanism, we well explain what the model learns, helping us build the mapping from the discovery of important sequential determinants to the in-depth analysis of their biological functions.
ISSN:1474-760X
1474-7596
1474-760X
DOI:10.1186/s13059-022-02780-1