ArticleBMC medical genomics2015
A fast and high performance multiple data integration algorithm for identifying human disease genes.
Article in BMC medical genomics, 2015. 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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13 citing papers in PubMed, 46 citations in OpenAlex.
- Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling.Bioinformatics (Oxford, England) · 2023Article
- NeuRank: learning to rank with neural networks for drug-target interaction prediction.BMC bioinformatics · 2021Article
- Gene Network Analysis of Alzheimer's Disease Based on Network and Statistical Methods.Entropy (Basel, Switzerland) · 2021Article
- Ensemble disease gene prediction by clinical sample-based networks.BMC bioinformatics · 2020Article
- MultiSourcDSim: an integrated approach for exploring disease similarity.BMC medical informatics and decision making · 2019Article
- Identifying Cancer genes by combining two-rounds RWR based on multiple biological data.BMC bioinformatics · 2019Article
- Integrating random walk and binary regression to identify novel miRNA-disease association.BMC bioinformatics · 2019Article
- pBRIT: gene prioritization by correlating functional and phenotypic annotations through integrative data fusion.Bioinformatics (Oxford, England) · 2018Article
- Scuba: scalable kernel-based gene prioritization.BMC bioinformatics · 2018Article
- An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data.BioMed research international · 2018Article
- Predicting combinative drug pairs towards realistic screening via integrating heterogeneous features.BMC bioinformatics · 2017Article
- Constructing an integrated gene similarity network for the identification of disease genes.Journal of biomedical semantics · 2017Article
- Predicting disease-related genes using integrated biomedical networks.BMC genomics · 2017Article
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIntegrating multiple data sources is indispensable in improving disease gene identification. It is not only due to the fact that disease genes associated with similar genetic diseases tend to lie close with each other in various biological networks, but also due to the fact that gene-disease associations are complex. Although various algorithms have been proposed to identify disease genes, their prediction performances and the computational time still should be further improved.
resultsIn this study, we propose a fast and high performance multiple data integration algorithm for identifying human disease genes. A posterior probability of each candidate gene associated with individual diseases is calculated by using a Bayesian analysis method and a binary logistic regression model. Two prior probability estimation strategies and two feature vector construction methods are developed to test the performance of the proposed algorithm.
conclusionsThe proposed algorithm is not only generated predictions with high AUC scores, but also runs very fast. When only a single PPI network is employed, the AUC score is 0.769 by using F2 as feature vectors. The average running time for each leave-one-out experiment is only around 1.5 seconds. When three biological networks are integrated, the AUC score using F3 as feature vectors increases to 0.830, and the average running time for each leave-one-out experiment takes only about 12.54 seconds. It is better than many existing algorithms.
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