Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk.
Computational techniques for foreseeing drug-disease associations by means of incorporating gene expression as well as biological network give high intuitions to the composite associations amongst targets, drugs, disease genes in addition to the diseases at a system level. Hepatocellular Carcinoma (...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 10; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2018
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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=132085513&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132085513 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2018 vid: 42 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132085513 132085513 132085513 10.1007/s10916-018-1038-y 132085513 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of Novel Drugs and Diseases for Hepatocellular Carcinoma Based on Multi-Source Simulated Annealing Based Random Walk. aug: au: Ibrahim, S. Jafar Ali Thangamani, M. affil: Anna University, Chennai, Tamilnadu, India sug: subj: Carcinoma, Hepatocellular Drug Therapy Drug Discovery Gene Expression Algorithms Human Carcinoma, Hepatocellular Complications Drug Information Drug Interactions Precision Computer Simulation Descriptive Statistics Conceptual Framework Models, Theoretical ab: Computational techniques for foreseeing drug-disease associations by means of incorporating gene expression as well as biological network give high intuitions to the composite associations amongst targets, drugs, disease genes in addition to the diseases at a system level. Hepatocellular Carcinoma (HCC) is a malevolent tumor containing a greater rate of sickness as well as mortality. In the present work, an Integrative framework is presented with the aim of resolving this problem, for identifying new Drugs for HCC dependent upon Multi-Source Random Walk (PD-MRW), in which score the complete drugs by means of building the drug-drug similarity network. On the other hand, the collection of clinical phenotypes as well as drug side effects in combination with patient-specific genetic info. As a result, the formation of disease-drug networks that denotes the prescriptions, which are allotted to treat those diseases that are not concentrated by means of PD-MRW model. With the aim of overcoming this issue, this research offers an integrative framework for foreseeing new drugs as well as diseases for HCC dependent upon Multi-Source Simulated Annealing based Random Walk (PDD-MSSARW). Primarily, build a Gene-Gene Weighted Interaction Network (GWIN), dependent upon the gene expression as well as protein interaction network. After that, construct a drug-drug similarity network, dependent upon multi-source random walk in GWIN, disease-drug similarity network with the help of Similarity Weighted Bipartite Graph Network (SWBGN) that is build up in which the nodes are drugs as well as association among one node to another node that explains the disease diagnoses. Lastly, dependent upon the known drugs for HCC, score the entire drugs in the similarity networks. The sturdiness of the likelihoods, their overlap with those stated in Comparative Toxicogenomics Database (CTD) as well as kinds of literature, and their enhanced KEGG pathway illustrate PDD-MSSARW method be capable of efficiently find out novel drug signs. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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