ProSim: A Method for Prioritizing Disease Genes Based on Protein Proximity and Disease Similarity.
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.
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Gamage Upeksha
GU
Ganegoda
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Yu
Y
Sheng
School of Information Science and Engineering, Central South University, Changsha 410083, China.
Jianxin
J
Wang
D000465:Algorithms; D000544:Alzheimer Disease / Q000235:genetics; D001943:Breast Neoplasms / Q000235:genetics; D015179:Colorectal Neoplasms / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D030541:Databases, Genetic; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D005260:Female; D030342:Genetic Diseases, Inborn / Q000235:genetics*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics; D008297:Male; D011471:Prostatic Neoplasms / Q000235:genetics; D060066:Protein Interaction Maps / Q000235:genetics*