ArticlePLoS computational biology2019
Disease gene prediction for molecularly uncharacterized diseases.
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 13 papers.
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Who cites it
13 citing papers in PubMed, 37 citations in OpenAlex.
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- Comparing gene-gene co-expression network approaches for the analysis of cell differentiation and specification on scRNAseq data.Computational and structural biotechnology journal · 2025Article
- LanDis: the disease landscape explorer.European journal of human genetics : EJHG · 2024Article
- Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View.Genes · 2022Review
- A Bayesian framework to integrate multi-level genome-scale data for Autism risk gene prioritization.BMC bioinformatics · 2022Article
- Machine learning and network medicine approaches for drug repositioning for COVID-19.Patterns (New York, N.Y.) · 2022Article
- MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction.Genes · 2021Article
- Machine learning applications for therapeutic tasks with genomics data.Patterns (New York, N.Y.) · 2021Review
- Diagnosis of corona diseases from associated genes and X-ray images using machine learning algorithms and deep CNN.Informatics in medicine unlocked · 2021Article
- Predicting the frequencies of drug side effects.Nature communications · 2020Article
- Supervised learning is an accurate method for network-based gene classification.Bioinformatics (Oxford, England) · 2020Article
- Network modeling of patients' biomolecular profiles for clinical phenotype/outcome prediction.Scientific reports · 2020Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Network medicine approaches have been largely successful at increasing our knowledge of molecularly characterized diseases. Given a set of disease genes associated with a disease, neighbourhood-based methods and random walkers exploit the interactome allowing the prediction of further genes for that disease. In general, however, diseases with no known molecular basis constitute a challenge. Here we present a novel network approach to prioritize gene-disease associations that is able to also predict genes for diseases with no known molecular basis. Our method, which we have called Cardigan (ChARting DIsease Gene AssociatioNs), uses semi-supervised learning and exploits a measure of similarity between disease phenotypes. We evaluated its performance at predicting genes for both molecularly characterized and uncharacterized diseases in OMIM, using both weighted and binary interactomes, and compared it with state-of-the-art methods. Our tests, which use datasets collected at different points in time to replicate the dynamics of the disease gene discovery process, prove that Cardigan is able to accurately predict disease genes for molecularly uncharacterized diseases. Additionally, standard leave-one-out cross validation tests show how our approach outperforms state-of-the-art methods at predicting genes for molecularly characterized diseases by 14%-65%. Cardigan can also be used for disease module prediction, where it outperforms state-of-the-art methods by 87%-299%.
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