ArticleBioData mining2011
DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization.
Article in BioData mining, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers.
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81 citing papers in PubMed, 220 citations in OpenAlex.
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- MELGene: knowledge-enhanced multimodel ensemble learning for disease-gene association prediction.Briefings in bioinformatics · 2026Article
- NetMedPy: a Python package for large-scale network medicine screening.Bioinformatics (Oxford, England) · 2025Article
- Bias-aware training and evaluation of link prediction algorithms in network biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- AMEND 2.0: module identification and multi-omic data integration with multiplex-heterogeneous graphs.BMC bioinformatics · 2025Article
- Prioritizing cancer therapeutic genes using BioRank: A biologically-informed PageRank framework.Computational and structural biotechnology journal · 2025Article
- Identifying Alzheimer's disease-associated genes using PhenoGeneRanker.bioRxiv : the preprint server for biology · 2024Article
- A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation.BMC bioinformatics · 2024Article
- KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.Briefings in bioinformatics · 2024Article
- Network propagation for GWAS analysis: a practical guide to leveraging molecular networks for disease gene discovery.Briefings in bioinformatics · 2024Review
- A network-based method for associating genes with autism spectrum disorder.Frontiers in bioinformatics · 2024Article
- AMEND: active module identification using experimental data and network diffusion.BMC bioinformatics · 2023Article
- Mapping the common gene networks that underlie related diseases.Nature protocols · 2023Review
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks.International journal of molecular sciences · 2022Article
- Deeper insights into long-term survival heterogeneity of pancreatic ductal adenocarcinoma (PDAC) patients using integrative individual- and group-level transcriptome network analyses.Scientific reports · 2022Article
- Network-based machine learning approach to predict immunotherapy response in cancer patients.Nature communications · 2022Article
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21 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundHigh-throughput molecular interaction data have been used effectively to prioritize candidate genes that are linked to a disease, based on the observation that the products of genes associated with similar diseases are likely to interact with each other heavily in a network of protein-protein interactions (PPIs). An important challenge for these applications, however, is the incomplete and noisy nature of PPI data. Information flow based methods alleviate these problems to a certain extent, by considering indirect interactions and multiplicity of paths.
resultsWe demonstrate that existing methods are likely to favor highly connected genes, making prioritization sensitive to the skewed degree distribution of PPI networks, as well as ascertainment bias in available interaction and disease association data. Motivated by this observation, we propose several statistical adjustment methods to account for the degree distribution of known disease and candidate genes, using a PPI network with associated confidence scores for interactions. We show that the proposed methods can detect loosely connected disease genes that are missed by existing approaches, however, this improvement might come at the price of more false negatives for highly connected genes. Consequently, we develop a suite called DADA, which includes different uniform prioritization methods that effectively integrate existing approaches with the proposed statistical adjustment strategies. Comprehensive experimental results on the Online Mendelian Inheritance in Man (OMIM) database show that DADA outperforms existing methods in prioritizing candidate disease genes.
conclusionsThese results demonstrate the importance of employing accurate statistical models and associated adjustment methods in network-based disease gene prioritization, as well as other network-based functional inference applications. DADA is implemented in Matlab and is freely available at http://compbio.case.edu/dada/.
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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.