ArticleNAR genomics and bioinformatics2021
ParsVNN: parsimony visible neural networks for uncovering cancer-specific and drug-sensitive genes and pathways.
Article in NAR genomics and bioinformatics, 2021. 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
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- TF-GateNet: An Interpretable and Biologically Guided Framework for Primary-Metastatic State Prediction from Somatic Genomic Alterations.Biomolecules · 2026Article
- Foundation models and deep learning for cancer drug response prediction: a framework for data, metrics, and validation.Briefings in bioinformatics · 2026Review
- Interpretable neural network for risk stratification and drug target discovery based on PBMC transcriptomes.iScience · 2026Article
- From Black Box to Biological Insight: AttentioFuse Unlocks Multi-Omics Dynamics in Lung Cancer.Cancers · 2026Article
- Using Biological Networks to Guide Biomedical Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- A Transfer Learning Framework for Predicting and Interpreting Drug Responses via Single-Cell RNA-Seq Data.International journal of molecular sciences · 2025Article
- Strategies to include prior knowledge in omics analysis with deep neural networks.Patterns (New York, N.Y.) · 2025Review
- The Hallmarks of Predictive Oncology.Cancer discovery · 2025Article
- Pathway-guided architectures for interpretable AI in biological research.Computational and structural biotechnology journal · 2025Review
- Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.NPJ systems biology and applications · 2024Article
- A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.Nature cancer · 2024Article
- Cancer Mutations Converge on a Collection of Protein Assemblies to Predict Resistance to Replication Stress.Cancer discovery · 2024Article
- Reliable interpretability of biology-inspired deep neural networks.NPJ systems biology and applications · 2023Article
- Microbial Gene Ontology informed deep neural network for microbe functionality discovery in human diseases.PloS one · 2023Article
- XMR: an explainable multimodal neural network for drug response prediction.Frontiers in bioinformatics · 2023Article
- Opportunities and challenges in interpretable deep learning for drug sensitivity prediction of cancer cells.Frontiers in bioinformatics · 2022Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of cancer-specific drug responses as well as identification of the corresponding drug-sensitive genes and pathways remains a major biological and clinical challenge. Deep learning models hold immense promise for better drug response predictions, but most of them cannot provide biological and clinical interpretability. Visible neural network (VNN) models have emerged to solve the problem by giving neurons biological meanings and directly casting biological networks into the models. However, the biological networks used in VNNs are often redundant and contain components that are irrelevant to the downstream predictions. Therefore, the VNNs using these redundant biological networks are overparameterized, which significantly limits VNNs' predictive and explanatory power. To overcome the problem, we treat the edges and nodes in biological networks used in VNNs as features and develop a sparse learning framework ParsVNN to learn parsimony VNNs with only edges and nodes that contribute the most to the prediction task. We applied ParsVNN to build cancer-specific VNN models to predict drug response for five different cancer types. We demonstrated that the parsimony VNNs built by ParsVNN are superior to other state-of-the-art methods in terms of prediction performance and identification of cancer driver genes. Furthermore, we found that the pathways selected by ParsVNN have great potential to predict clinical outcomes as well as recommend synergistic drug combinations.
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Registered trials
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.