Modeling association detection in order to discover compounds to inhibit oral cancer.
In the past, algorithms exploiting varying semantics in interactions between biological objects such as genes and diseases have been used in bioinformatics to uncover latent relationships within biological datasets. In this paper, we consider the algorithm Medusa in parallel with binary classificati...
| Publicado en: | Journal of Biomedical Informatics Vol. 84; pp. 159 - 164 |
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| Autores principales: | , |
| Formato: | Journal Article |
| Publicado: |
Academic Press Inc.
Aug2018
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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=130990544&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130990544 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15320464 OMB jtl: Journal of Biomedical Informatics issn: 15320464 maglogo: N pubinfo: dt: Aug2018 vid: 84 pid: 735 pub: Academic Press Inc. place: Burlington, Massachusetts artinfo: ui: 130990544 130990544 NLM30004020 10.1016/j.jbi.2018.07.005 NLM30004020 130990544 ppf: 159 ppct: 5 formats: tig: atl: Modeling association detection in order to discover compounds to inhibit oral cancer. aug: au: Vittal, Suhas Karthikeyan, Gokul affil: BASIS Scottsdale, Scottsdale, AZ, United States sug: subj: Antineoplastic Agents Pharmacodynamics Bioinformatics Drugs Mouth Neoplasms Drug Therapy Semantics Transferases Antagonists and Inhibitors Peptide Hydrolases Antagonists and Inhibitors Chemistry, Pharmaceutical Reproducibility of Results Algorithms Mutation Drug Design Computer Simulation Resource Databases ab: In the past, algorithms exploiting varying semantics in interactions between biological objects such as genes and diseases have been used in bioinformatics to uncover latent relationships within biological datasets. In this paper, we consider the algorithm Medusa in parallel with binary classification in order to find potential compounds to inhibit oral cancer. Oral cancer affects the mouth and pharynx and has a high mortality rate due to its late discovery. Current methods of oral cancer treatment, such as chemoradiation and surgery, fail to provide better chances for survival, warranting an alternative approach. By running Medusa on a data fusion graph consisting of biological objects, we incorporate binary classification to model the algorithm's association detection to discover compounds with the potential to mitigate the effects of oral cancer. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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