ArticleNature communications2023
Interpreting biologically informed neural networks for enhanced proteomic biomarker discovery and pathway analysis.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
56 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
- Knowledge-driven interpretable neural networks provide mechanistic insight.Genome research · 2026Article
- Biologically informed neural network models are robust to spurious interactions via self-pruning.Bioinformatics (Oxford, England) · 2026Article
- Sparsity is all you need: rethinking biologically informed neural networks.Briefings in bioinformatics · 2026Article
- Navigating AI and machine learning in cancer research: an end-to-end translational framework.Journal of translational medicine · 2026Review
- Discovery-Driven Plasma Proteomics Identifies a Multi-Protein Signature for Amyloid PET Positivity: A Machine Learning Analysis of the Bio-Hermes Cohort.International journal of molecular sciences · 2026Article
- Deep knowledge-driven multi-modal fusion for diagnosis and prognosis of SI-ARDS.Communications medicine · 2026Article
- CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis.NPJ digital medicine · 2026Article
- Transcriptome graph transformer: a graph transformer-based unsupervised model for transcriptome data analysis.BMC bioinformatics · 2026Article
- Exploring the prognostic role of senescence-related genes in gastric cancer through multi-omics integration and machine learning.Human genomics · 2026Article
- Proteomic signatures of the APOE ε4 and APOE ε2 genetic variants and Alzheimer's disease.Nature aging · 2026Article
- Expanding P-NET, a multi-purpose biologically informed deep learning framework.bioRxiv : the preprint server for biology · 2026Article
- Interpretable neural network for risk stratification and drug target discovery based on PBMC transcriptomes.iScience · 2026Article
- Clinical peptidomics for respiratory diseases: matrices, workflows, and translation towards treatable traits, with a focus on COPD.Clinical proteomics · 2026Review
- The role of AI in oncology: present applications and future horizons.NPJ precision oncology · 2026Review
- Generative design of synthetic gene circuits for functional and evolutionary properties.NPJ systems biology and applications · 2026Article
- Transforming Intracerebral Hemorrhage Care with Artificial Intelligence: Opportunities, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2026Review
- CLinNET: An Interpretable and Uncertainty-Aware Deep Learning Framework for Multi-Modal Clinical Genomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A tri-omics and machine learning framework identifies prognostic biomarkers and metabolic signatures in sepsis.Scientific reports · 2026Article
- Artificial Neural Network Elucidates the Role of Transport Proteins inMetabolites · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The incorporation of machine learning methods into proteomics workflows improves the identification of disease-relevant biomarkers and biological pathways. However, machine learning models, such as deep neural networks, typically suffer from lack of interpretability. Here, we present a deep learning approach to combine biological pathway analysis and biomarker identification to increase the interpretability of proteomics experiments. Our approach integrates a priori knowledge of the relationships between proteins and biological pathways and biological processes into sparse neural networks to create biologically informed neural networks. We employ these networks to differentiate between clinical subphenotypes of septic acute kidney injury and COVID-19, as well as acute respiratory distress syndrome of different aetiologies. To gain biological insight into the complex syndromes, we utilize feature attribution-methods to introspect the networks for the identification of proteins and pathways important for distinguishing between subtypes. The algorithms are implemented in a freely available open source Python-package ( https://github.com/InfectionMedicineProteomics/BINN ).
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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 Socratic graph.