Search

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]

With the knowledge of molecular biology it is highlighting that long non-coding RNAs (lncRNAs) play a critical role in many important biological processes, such as imprinting control, cell differentiation, immune responses, human diseases, tumorigenesis and other biological processes. This study proposes a novel computational method, named KRWRH, to infer disease-lincRNA associations with the influence of phenotype information and tissue expression details of lincRNA. Gaussian interaction profile kernel is calculated for diseases and lincRNAs and random walk with restart method is used for final prediction. The proposed method KRWRH is compared with four existing methods: LRLSLDA, TslncRNA, NRWRH, and RWRH. The experimental results based on the leave-one-out cross validation, ROC curves, and mean enrichment show that the proposed method KRWRH is able to predict known and unknown disease-lincRNA associations more effectively. [ABSTRACT FROM PUBLISHER]