ArticleBMC bioinformatics2020
Biological interpretation of deep neural network for phenotype prediction based on gene expression.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- Integrative interpretable learning reveals shared patterns of epitranscriptomic regulation across multiple cancer types.BMC biology · 2026Article
- G2PDeep-v2: A Web-Based Deep-Learning Framework for Phenotype Prediction and Biomarker Discovery for All Organisms Using Multi-Omics Data.Biomolecules · 2025Article
- Phenotypic and Gene Expression Alterations in Aquatic Organisms Exposed to Microplastics.International journal of molecular sciences · 2025Review
- DeepAnnotation: A novel interpretable deep learning-based genomic selection model that integrates comprehensive functional annotations.GigaScience · 2025Article
- Interpretable deep cross networks unveiled common signatures of dysregulated epitranscriptomes across 12 cancer types.Molecular therapy. Nucleic acids · 2024Article
- Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.NAR genomics and bioinformatics · 2024Article
- XModNN: Explainable Modular Neural Network to Identify Clinical Parameters and Disease Biomarkers in Transcriptomic Datasets.Biomolecules · 2024Article
- Leveraging explainable deep learning methodologies to elucidate the biological underpinnings of Huntington's disease using single-cell RNA sequencing data.BMC genomics · 2024Article
- A penalized integrative deep neural network for variable selection among multiple omics datasets.Quantitative biology (Beijing, China) · 2024Article
- Exploring combinations of dimensionality reduction, transfer learning, and regularization methods for predicting binary phenotypes with transcriptomic data.BMC bioinformatics · 2024Article
- XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.PLoS computational biology · 2023Article
- Deep learning explains the biology of branched glycans from single-cell sequencing data.iScience · 2022Article
- Identifying common transcriptome signatures of cancer by interpreting deep learning models.Genome biology · 2022Article
- MOMA: a multi-task attention learning algorithm for multi-omics data interpretation and classification.Bioinformatics (Oxford, England) · 2022Article
- Article
- XOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data.Briefings in bioinformatics · 2021Article
- Topic Evolution Analysis for Omics Data Integration in Cancers.Frontiers in cell and developmental biology · 2021Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field.
resultsWe focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods on large training sets; (2) our approach produces interpretations more coherent with biology than the state-of-the-art based approaches; (3) we can provide a comprehensive explanation of the predictions for biologists and physicians.
conclusionWe propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data. Since the model can find relationships between the phenotype and gene expression, we may assume that there is a link between the identified genes and the phenotype. The interpretation can, therefore, lead to new biological hypotheses to be investigated by biologists.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.