Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 61; no. 11; pp. 2895 - 2920 |
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| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2023
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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=173036049&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 173036049 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2023 vid: 61 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 173036049 10.1007/s11517-023-02892-1 173036049 ppf: 2895 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Enhancing the prediction of IDC breast cancer staging from gene expression profiles using hybrid feature selection methods and deep learning architecture. aug: au: Kishore, Akash Venkataramana, Lokeswari Prasad, D. Venkata Vara Mohan, Akshaya Jha, Bhavya affil: https://ror.org/054psm803 Department of CSE, Sri Sivasubramaniya Nadar College of Engineering, Kalavakkam, Chennai, India sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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