GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine.
Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment and refinement of the effects that the somatic cells show towards other clinical conditions. The genome of these somatic cells exhibits mutations and genetic variations of the breast cancer cells and...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 11; pp. 2483 - 2516 |
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| Autor principal: | |
| Formato: | Journal Article |
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Springer Nature
Nov2019
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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=139479826&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 139479826 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2019 vid: 57 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 139479826 139479826 NLM31591679 10.1007/s11517-019-02038-2 NLM31591679 139479826 ppf: 2483 ppct: 33 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: GSIAR: gene-subcategory interaction-based improved deep representation learning for breast cancer subcategorical analysis using gene expression, applicable for precision medicine. aug: au: Sur, Chiranjib affil: Computer & Information Science & Engineering Department, University of Florida, Gainesville, FL, USA sug: subj: Breast Neoplasms Proteomics Proteomics Methods Breast Neoplasms Classification Proteomics Classification Algorithms Resource Databases Female Scales Female ab: Tumor subclass detection and diagnosis is inevitable requirement for personalized medical treatment and refinement of the effects that the somatic cells show towards other clinical conditions. The genome of these somatic cells exhibits mutations and genetic variations of the breast cancer cells and helps in understanding the characteristic behavior of the cancer cells. But their analysis is limited to clustering and there is requirement to analyze what else can be done with the data for identifying the tumor subcategory and the stages of subclasses. In this work, we have extended the work with similar data (consisting of 105 breast tumor cell lines) to solve other detection and characterization problems through computation and intelligent representation learning. Most of our work comprises of systematic data cleaning, analysis, and building prediction models with deep computational architectures and establish that the transformed data can help in better distinction of the respective categories. Our main contribution is the novel gene-subcategory interaction-based regularization (GSIAR) based data selection and analysis concept, alongside the prediction, proven to enhance the performance of the classification techniques. Graphical Abstract A graphical abstract of our model - Gene-subcategory interaction affinity-based regularization (GSIAR). pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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