ArticleFrontiers in cell and developmental biology2021
4mCPred-MTL: Accurate Identification of DNA 4mC Sites in Multiple Species Using Multi-Task Deep Learning Based on Multi-Head Attention Mechanism.
Article in Frontiers in cell and developmental biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
- Prediction of DNA Methylation based on Multi-dimensional feature encoding and double convolutional fully connected convolutional neural network.PLoS computational biology · 2023Article
- Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review.Biology · 2023Review
- i4mC-GRU: Identifying DNA NComputational and structural biotechnology journal · 2023Article
- MuLan-Methyl-multiple transformer-based language models for accurate DNA methylation prediction.GigaScience · 2022Article
- Systematic Analysis and Accurate Identification of DNA N4-Methylcytosine Sites by Deep Learning.Frontiers in microbiology · 2022Article
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Authors and funding
3 authors.
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Abstract
DNA methylation is one of the most extensive epigenetic modifications. DNA 4mC modification plays a key role in regulating chromatin structure and gene expression. In this study, we proposed a generic 4mC computational predictor, namely, 4mCPred-MTL using multi-task learning coupled with Transformer to predict 4mC sites in multiple species. In this predictor, we utilize a multi-task learning framework, in which each task is to train species-specific data based on Transformer. Extensive experimental results show that our multi-task predictive model can significantly improve the performance of the model based on single task and outperform existing methods on benchmarking comparison. Moreover, we found that our model can sufficiently capture better characteristics of 4mC sites as compared to existing commonly used feature descriptors, demonstrating the strong feature learning ability of our model. Therefore, based on the above results, it can be expected that our 4mCPred-MTL can be a useful tool for research communities of interest.
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