ArticleCommunications biology2020
A gene prioritization method based on a swine multi-omics knowledgebase and a deep learning model.
Article in Communications biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed.
- Multi-Layer Transcriptomic Comparison Across Human, Pig, and Mouse Informs Tissue- and Trait-Specific Animal Model Selection.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Multi-omics integration and artificial intelligence for the conservation and utilization of local chicken genetic resources.Poultry science · 2026Review
- FOS Regulates Myogenic and Adipogenic Differentiation via Extracellular Matrix Signaling.Journal of agricultural and food chemistry · 2026Article
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Pig Matrix: a matched multiomics 3D regulatory genomics database for evolutionary and comparative analyses in pigs.Molecular biology and evolution · 2026Article
- Integrated multi-population selection-signature scans identify putative functional genes influencing litter size in jishen black pigs.BMC genomics · 2026Article
- Genome-Wide Association Study Revealed Key Genes with the Teat Number in Jishen Black Pigs.Veterinary sciences · 2026Article
- Genetic evidence of population admixture with European pigs and Southern Chinese pigs in Chinese indigenous Diqing Tibetan breed.BMC genomics · 2026Article
- Tissue-specific transfer learning improves functional variant and therapeutic target discoveries in breast and prostate cancer.PLoS genetics · 2026Article
- Role of multi-omics in aquaculture genetics and breeding: current status and future perspective.Science China. Life sciences · 2025Review
- Genome-Wide Association Study of Gluteus Medius Muscle Size in a Crossbred Pig Population.Veterinary sciences · 2025Article
- Genomic Analysis of Reproductive Trait Divergence in Duroc and Yorkshire Pigs: A Comparison of Mixed Models and Selective Sweep Detection.Veterinary sciences · 2025Article
- A comparative review of short genetic variant databases across humans and animal species.Briefings in bioinformatics · 2025Review
- PIGOME: An Integrated and Comprehensive Multi-omics Database for Pig Functional Genomics Studies.Genomics, proteomics & bioinformatics · 2025Article
- DeepAnnotation: A novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations.GigaScience · 2025Article
- Omics sciences for cervical cancer precision medicine from the perspective of the tumor immune microenvironment.Oncology research · 2025Review
- Genome-wide analysis of genetic loci and candidate genes related to teat number traits in Dongliao black pigs.Frontiers in genetics · 2025Article
- Tabular deep learning: a comparative study applied to multi-task genome-wide prediction.BMC bioinformatics · 2024Article
- Pathformer: a biological pathway informed transformer for disease diagnosis and prognosis using multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- Identification of different myofiber types in pigs muscles and construction of regulatory networks.BMC genomics · 2024Article
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Authors and funding
20 authors.
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Abstract
The analyses of multi-omics data have revealed candidate genes for objective traits. However, they are integrated poorly, especially in non-model organisms, and they pose a great challenge for prioritizing candidate genes for follow-up experimental verification. Here, we present a general convolutional neural network model that integrates multi-omics information to prioritize the candidate genes of objective traits. By applying this model to Sus scrofa, which is a non-model organism, but one of the most important livestock animals, the model precision was 72.9%, recall 73.5%, and F1-Measure 73.4%, demonstrating a good prediction performance compared with previous studies in Arabidopsis thaliana and Oryza sativa. Additionally, to facilitate the use of the model, we present ISwine ( http://iswine.iomics.pro/ ), which is an online comprehensive knowledgebase in which we incorporated almost all the published swine multi-omics data. Overall, the results suggest that the deep learning strategy will greatly facilitate analyses of multi-omics integration in the future.
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