ArticleGenome biology2020
Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.
Article in Genome biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers, 3 of them syntheses that pooled it.
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Who cites it
77 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Visible neural networks for multi-omics integration: a critical review.Frontiers in artificial intelligence · 2025Pooled it
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- A Systematic Review of the Tumor-Infiltrating CD8Frontiers in immunology · 2021Pooled it
- Review
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.Cell systems · 2026Article
- Human genetics across levels of biological organization.Nature reviews. Genetics · 2026Review
- Data-intensive immune network modelling for One Health.Briefings in bioinformatics · 2026Review
- Computational blueprints for cell fate programming.Stem cell reports · 2026Review
- Simulation and empirical evaluation of biologically-informed neural network performance.Machine learning with applications · 2026Article
- Interpretable neural network for risk stratification and drug target discovery based on PBMC transcriptomes.iScience · 2026Article
- Reconstructing and comparing signal transduction networks from single-cell protein quantification data.Bioinformatics (Oxford, England) · 2026Article
- BioLM-NET: an interpretable deep learning model combining prior biological knowledge and contextual LLM gene embeddings on multi-omics data to predict disease.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- Using Biological Networks to Guide Biomedical Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Graph convolutional networks for inferring cell-cell communication from spatial transcriptomics data.Bioinformatics advances · 2026Article
- scKGBERT: a knowledge-enhanced foundation model for single-cell transcriptomics.Genome biology · 2025Article
- Simulation and empirical evaluation of biologically-informed neural network performance.bioRxiv : the preprint server for biology · 2025Article
- Decoding lysophosphatidic acid signaling in physiology and disease: mapping the multimodal and multinodal signaling networks.Signal transduction and targeted therapy · 2025Review
- Interpretable biophysical neural networks of transcriptional activation domains separate roles of protein abundance and coactivator binding.bioRxiv : the preprint server for biology · 2025Article
- Deep fusion of incomplete multi-omic data for molecular mechanism of Alzheimer's disease.Scientific reports · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
17 more citing papers are in PubMed but not listed here.
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2 authors.
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Abstract
backgroundDeep learning has emerged as a versatile approach for predicting complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the biological mechanisms that underlie a successful prediction. Here we demonstrate deep learning on biological networks, where every node has a molecular equivalent, such as a protein or gene, and every edge has a mechanistic interpretation, such as a regulatory interaction along a signaling pathway.
resultsWith knowledge-primed neural networks (KPNNs), we exploit the ability of deep learning algorithms to assign meaningful weights in multi-layered networks, resulting in a widely applicable approach for interpretable deep learning. We present a learning method that enhances the interpretability of trained KPNNs by stabilizing node weights in the presence of redundancy, enhancing the quantitative interpretability of node weights, and controlling for uneven connectivity in biological networks. We validate KPNNs on simulated data with known ground truth and demonstrate their practical use and utility in five biological applications with single-cell RNA-seq data for cancer and immune cells.
conclusionsWe introduce KPNNs as a method that combines the predictive power of deep learning with the interpretability of biological networks. While demonstrated here on single-cell sequencing data, this method is broadly relevant to other research areas where prior domain knowledge can be represented as networks.
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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.