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 prot...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 12 |
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| Autores principales: | , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/3/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109030968&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109030968 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/3/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109030968 109030968 109030968 10.1155/2015/213750 109030968 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: ProSim: A Method for Prioritizing Disease Genes Based on Protein Proximity and Disease Similarity. aug: au: Ganegoda, Gamage Upeksha Sheng, Yu Wang, Jianxin affil: School of Information Science and Engineering, Central South University, Changsha 410083, China sug: subj: Genetics, Medical Bioinformatics Methods Algorithms Phenotype Gene Expression Proteins Data Mining Human Funding Source Case Studies Prostatic Neoplasms Familial and Genetic Alzheimer's Disease Familial and Genetic Diabetes Mellitus, Type 2 Familial and Genetic Breast Neoplasms Familial and Genetic Colorectal Neoplasms Familial and Genetic Lung Neoplasms Familial and Genetic ROC Curve Databases, Health Gene Expression Profiling Logistic Regression Pearson's Correlation Coefficient Cellular Structures Sequence Analysis Sensitivity and Specificity Descriptive Statistics Disease Susceptibility Familial and Genetic ab: 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. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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