ArticlePLoS computational biology2019
Benchmarking network propagation methods for disease gene identification.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
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- Finding Significant Hits in Networks: a network-based tool for analyzing gene-level P-values to identify significant genes missed by standard methods.Briefings in bioinformatics · 2026Article
- Decoding non-coding SNPs: systems genomics modelling dissects the heterogeneity of IBD.Molecular systems biology · 2026Article
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- Confounder-Adjusted Differentiation of Colorectal Cancer via Dynamic Propagation of Pathway Influence.International journal of molecular sciences · 2025Article
- RWRtoolkit: multi-omic network analysis using random walks on multiplex networks in any species.GigaScience · 2025Article
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- Imperfect gold standard gene sets yield inaccurate evaluation of causal gene identification methods.Communications biology · 2024Review
- Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery.Briefings in bioinformatics · 2024Review
- Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.BMC bioinformatics · 2023Article
- Dorsal Hand Vein Pattern Recognition: A Comparison between Manual and Automatic Segmentation Methods.Healthcare informatics research · 2023Article
- NIAPU: network-informed adaptive positive-unlabeled learning for disease gene identification.Bioinformatics (Oxford, England) · 2023Article
- Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships.Patterns (New York, N.Y.) · 2023Article
- Drug repositioning in thyroid cancer treatment: the intriguing case of anti-diabetic drugs.Frontiers in pharmacology · 2023Review
- NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review.Frontiers in genetics · 2022Review
- Knowledge Graphs for Indication Expansion: An Explainable Target-Disease Prediction Method.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In-silico identification of potential target genes for disease is an essential aspect of drug target discovery. Recent studies suggest that successful targets can be found through by leveraging genetic, genomic and protein interaction information. Here, we systematically tested the ability of 12 varied algorithms, based on network propagation, to identify genes that have been targeted by any drug, on gene-disease data from 22 common non-cancerous diseases in OpenTargets. We considered two biological networks, six performance metrics and compared two types of input gene-disease association scores. The impact of the design factors in performance was quantified through additive explanatory models. Standard cross-validation led to over-optimistic performance estimates due to the presence of protein complexes. In order to obtain realistic estimates, we introduced two novel protein complex-aware cross-validation schemes. When seeding biological networks with known drug targets, machine learning and diffusion-based methods found around 2-4 true targets within the top 20 suggestions. Seeding the networks with genes associated to disease by genetics decreased performance below 1 true hit on average. The use of a larger network, although noisier, improved overall performance. We conclude that diffusion-based prioritisers and machine learning applied to diffusion-based features are suited for drug discovery in practice and improve over simpler neighbour-voting methods. We also demonstrate the large impact of choosing an adequate validation strategy and the definition of seed disease genes.
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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.