Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach.
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 11; pp. 2631 - 2641 |
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| Autores principales: | , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2020
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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=146433306&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146433306 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2020 vid: 58 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146433306 145295122 10.1007/s11517-020-02245-2 146433306 ppf: 2631 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach. aug: au: Mahmood, Nasir Shahid, Saman Bakhshi, Taimur Riaz, Sehar Ghufran, Hafiz Yaqoob, Muhammad affil: Department of Biochemistry, Human Genetics and Molecular Biology, University of Health Sciences (UHS), Lahore, Pakistan sug: pubtype: Academic Journal doctype: Journal Article ougenre: Article ab: language: English refInfo: holdings: @attributes: islocal: N |
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