1. ProSim: A Method for Prioritizing Disease Genes Based on Protein Proximity and Disease Similarity.
- Author
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Ganegoda, Gamage Upeksha, Sheng, Yu, and Wang, Jianxin
- Subjects
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GENETICS of Alzheimer's disease , *BREAST tumors , *GENETICS of disease susceptibility , *GENETICS of type 2 diabetes , *LUNG tumors , *PROSTATE tumors , *COLON tumors , *ALGORITHMS , *CELLS , *STATISTICAL correlation , *DATABASES , *GENE expression , *MEDICAL information storage & retrieval systems , *CASE studies , *MEDICAL genetics , *PROTEINS , *RESEARCH funding , *PHENOTYPES , *DATA mining , *LOGISTIC regression analysis , *BIOINFORMATICS , *RECEIVER operating characteristic curves , *GENE expression profiling , *DESCRIPTIVE statistics , *SEQUENCE analysis , *GENETICS ,RECTUM tumors - Abstract
Predicting disease genes for a particular genetic disease is very challenging in bioinformatics. Based on current research studies, this challenge can be tackled via network-based approaches. Furthermore, it has been highlighted that it is necessary to consider disease similarity along with the protein’s proximity to disease genes in a protein-protein interaction (PPI) network in order to improve the accuracy of disease gene prioritization. In this study we propose a new algorithm called proximity disease similarity algorithm (ProSim), which takes both of the aforementioned properties into consideration, to prioritize disease genes. To illustrate the proposed algorithm, we have conducted six case studies, namely, prostate cancer, Alzheimer’s disease, diabetes mellitus type 2, breast cancer, colorectal cancer, and lung cancer. We employed leave-one-out cross validation, mean enrichment, tenfold cross validation, and ROC curves to evaluate our proposed method and other existing methods. The results show that our proposed method outperforms existing methods such as PRINCE, RWR, and DADA. [ABSTRACT FROM AUTHOR]
- Published
- 2015
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