ArticleBioinformatics (Oxford, England)2022
Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 24 citations in OpenAlex.
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- TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.Plant communications · 2024Article
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
- A statistical approach for identifying single nucleotide variants that affect transcription factor binding.iScience · 2024Article
- Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data.Frontiers in digital health · 2024Review
- CVD-associated SNPs with regulatory potential reveal novel non-coding disease genes.Human genomics · 2023Article
- Harnessing the potential of machine learning and artificial intelligence for dementia research.Brain informatics · 2023Review
- TIVAN-indel: a computational framework for annotating and predicting non-coding regulatory small insertions and deletions.Bioinformatics (Oxford, England) · 2023Article
- DeepPHiC: predicting promoter-centered chromatin interactions using a novel deep learning approach.Bioinformatics (Oxford, England) · 2023Article
- Multi-task deep autoencoder to predict Alzheimer's disease progression using temporal DNA methylation data in peripheral blood.Computational and structural biotechnology journal · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationThough genome-wide association studies have identified tens of thousands of variants associated with complex traits and most of them fall within the non-coding regions, they may not be the causal ones. The development of high-throughput functional assays leads to the discovery of experimental validated non-coding functional variants. However, these validated variants are rare due to technical difficulty and financial cost. The small sample size of validated variants makes it less reliable to develop a supervised machine learning model for achieving a whole genome-wide prediction of non-coding causal variants.
resultsWe will exploit a deep transfer learning model, which is based on convolutional neural network, to improve the prediction for functional non-coding variants (NCVs). To address the challenge of small sample size, the transfer learning model leverages both large-scale generic functional NCVs to improve the learning of low-level features and context-specific functional NCVs to learn high-level features toward the context-specific prediction task. By evaluating the deep transfer learning model on three MPRA datasets and 16 GWAS datasets, we demonstrate that the proposed model outperforms deep learning models without pretraining or retraining. In addition, the deep transfer learning model outperforms 18 existing computational methods in both MPRA and GWAS datasets. AVAILABILITY AND IMPLEMENTATION: https://github.com/lichen-lab/TLVar. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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