ArticleBioinformatics (Oxford, England)2011
Uncover disease genes by maximizing information flow in the phenome-interactome network.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Genome-phenome association prediction using weighted deep matrix factorization with a multisource graph attention network.Quantitative biology (Beijing, China) · 2026Article
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data.Genomics, proteomics & bioinformatics · 2022Article
- Genome-wide discovery of hidden genes mediating known drug-disease association using KDDANet.NPJ genomic medicine · 2021Article
- Augmenting subnetwork inference with information extracted from the scientific literature.PLoS computational biology · 2019Article
- Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks.BMC medical genomics · 2018Article
- Gene gravity-like algorithm for disease gene prediction based on phenotype-specific network.BMC systems biology · 2017Article
- Discovery of Bladder Cancer-related Genes Using Integrative Heterogeneous Network Modeling of Multi-omics Data.Scientific reports · 2017Article
- Mimvec: a deep learning approach for analyzing the human phenome.BMC systems biology · 2017Article
- HGPEC: a Cytoscape app for prediction of novel disease-gene and disease-disease associations and evidence collection based on a random walk on heterogeneous network.BMC systems biology · 2017Article
- Context-sensitive network-based disease genetics prediction and its implications in drug discovery.Bioinformatics (Oxford, England) · 2017Article
- Large-scale prediction of microRNA-disease associations by combinatorial prioritization algorithm.Scientific reports · 2017Article
- Biomechanisms of Comorbidity: Reviewing Integrative Analyses of Multi-omics Datasets and Electronic Health Records.Yearbook of medical informatics · 2016Review
- Disease gene prioritization by integrating tissue-specific molecular networks using a robust multi-network model.BMC bioinformatics · 2016Article
- SoftPanel: a website for grouping diseases and related disorders for generation of customized panels.BMC bioinformatics · 2016Article
- Article
- Fundamentals of protein interaction network mapping.Molecular systems biology · 2015Review
- A new method to improve network topological similarity search: applied to fold recognition.Bioinformatics (Oxford, England) · 2015Article
- Phenome-driven disease genetics prediction toward drug discovery.Bioinformatics (Oxford, England) · 2015Article
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Authors and funding
3 authors.
Funding
Abstract
motivationPinpointing genes that underlie human inherited diseases among candidate genes in susceptibility genetic regions is the primary step towards the understanding of pathogenesis of diseases. Although several probabilistic models have been proposed to prioritize candidate genes using phenotype similarities and protein-protein interactions, no combinatorial approaches have been proposed in the literature.
resultsWe propose the first combinatorial approach for prioritizing candidate genes. We first construct a phenome-interactome network by integrating the given phenotype similarity profile, protein-protein interaction network and associations between diseases and genes. Then, we introduce a computational method called MAXIF to maximize the information flow in this network for uncovering genes that underlie diseases. We demonstrate the effectiveness of this method in prioritizing candidate genes through a series of cross-validation experiments, and we show the possibility of using this method to identify diseases with which a query gene may be associated. We demonstrate the competitive performance of our method through a comparison with two existing state-of-the-art methods, and we analyze the robustness of our method with respect to the parameters involved. As an example application, we apply our method to predict driver genes in 50 copy number aberration regions of melanoma. Our method is not only able to identify several driver genes that have been reported in the literature, it also shed some new biological insights on the understanding of the modular property and transcriptional regulation scheme of these driver genes. CONTACT: ruijiang@tsinghua.edu.cn.
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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.