Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach.
Numerous studies have been conducted to elucidate the relation of tumor proximity to cancer prognosis and treatment efficacy in colorectal cancer. However, the molecular pathways and prognoses of left- and right-sided colorectal cancers are different, and this difference has not been fully investiga...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 10; pp. 2877 - 2898 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Oct2022
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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=159003931&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159003931 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2022 vid: 60 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159003931 158444769 159003931 NLM35948841 10.1007/s11517-022-02641-w NLM35948841 159003931 ppf: 2877 ppct: 21 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identifying the tumor location-associated candidate genes in development of new drugs for colorectal cancer using machine-learning-based approach. aug: au: Bayrak, Tuncay Çetin, Zafer Saygılı, E. İlker Ogul, Hasan affil: Turkish Medicines and Medical Devices Agency, Ankara, Turkey sug: subj: Colorectal Neoplasms Drug Therapy Colorectal Neoplasms Genetic Research ab: Numerous studies have been conducted to elucidate the relation of tumor proximity to cancer prognosis and treatment efficacy in colorectal cancer. However, the molecular pathways and prognoses of left- and right-sided colorectal cancers are different, and this difference has not been fully investigated at the genomic level. In this study, a set of data science approaches, including six feature selection methods and three classification models, were used in predicting tumor location from gene expression profiles. Specificity, sensitivity, accuracy, and Mathew's correlation coefficient (MCC) evaluation metrics were used to evaluate the classification ability. Gene ontology enrichment analysis was applied by the Gene Ontology PANTHER Classification System. For the most significant 50 genes, protein-protein interactions and drug-gene interactions were analyzed using the GeneMANIA, CytoScape, CytoHubba, MCODE, and DGIdb databases. The highest classification accuracy (90%) is achieved with the most significant 200 genes when the ensemble-decision tree classification model is used with the ReliefF feature selection method. Molecular pathways and drug interactions are investigated for the most significant 50 genes. It is concluded that a machine-learning-based approach could be useful to discover the significant genes that may have an important role in the development of new therapies and drugs for colorectal cancer. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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