ArticleMolecular systems biology2019
netDx: interpretable patient classification using integrated patient similarity networks.
Article in Molecular systems biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
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Who cites it
47 citing papers in PubMed.
- Genomes From 117 Vertebrate Species Reveal Rapidly Evolving Segmental-Duplication Landscapes.Genome biology and evolution · 2026Article
- Spatial AI in cancer: mapping immune evasion topology through multi-modal omics and deep learning.Frontiers in oncology · 2026Review
- Learning Patient Similarity from Genomics for Precision Oncology.medRxiv : the preprint server for health sciences · 2025Article
- PatientProfiler: building patient-specific signaling models from proteogenomic data.Molecular systems biology · 2025Article
- Genomes from 117 vertebrate species reveal rapidly evolving segmental duplication landscapes.bioRxiv : the preprint server for biology · 2025Article
- Uncovering the Understanding of the Concept of Patient Similarity in Cancer Research and Treatment: Scoping Review.Journal of medical Internet research · 2025Article
- Host Plasma Microenvironment in Immunometabolically Impaired HIV Infection Leads to Dysregulated Monocyte Function and Synaptic Transmission Ex Vivo.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Disease Network-Based Approaches to Study Comorbidity in Heart Failure: Current State and Future Perspectives.Current heart failure reports · 2024Review
- Transcriptomic and epigenomic profiling reveals altered responses to diesel emissions in Alzheimer's disease both in vitro and in population-based data.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Multi-Omic Graph Diagnosis (MOGDx): a data integration tool to perform classification tasks for heterogeneous diseases.Bioinformatics (Oxford, England) · 2024Article
- Constructing a Clinical Patient Similarity Network of Gastric Cancer.Bioengineering (Basel, Switzerland) · 2024Article
- Graph machine learning for integrated multi-omics analysis.British journal of cancer · 2024Review
- Comparative analysis of integrative classification methods for multi-omics data.Briefings in bioinformatics · 2024Article
- StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling.PLoS computational biology · 2024Article
- A Patient Similarity Network (CHDmap) to Predict Outcomes After Congenital Heart Surgery: Development and Validation Study.JMIR medical informatics · 2024Article
- Unlocking the future of complex human diseases prediction: multi-omics risk score breakthrough.Frontiers in bioinformatics · 2024Article
- Current and future directions in network biology.Bioinformatics advances · 2024Article
- Integrating DNA methylation and gene expression data in a single gene network using the iNETgrate package.Scientific reports · 2023Article
- Explainable artificial intelligence for omics data: a systematic mapping study.Briefings in bioinformatics · 2023Review
- Constructing gene similarity networks using co-occurrence probabilities.BMC genomics · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis, and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks, which meets these criteria. In a cancer survival benchmark dataset integrating up to six data types in four cancer types, netDx significantly outperforms most other machine-learning approaches across most cancer types. Compared to traditional machine-learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in breast cancer and asthma. netDx can serve as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a free software implementation of netDx with automation workflows.
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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.