A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status
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A machine learning classifier trained on cancer transcriptomes detects NF1 inactivation signal in glioblastomaNext generation sequencing profiling identifies miR-574-3p and miR-660-5p as potential novel prognostic markers for breast cancer.Piwi-interacting RNAs and PIWI genes as novel prognostic markers for breast cancerNovel mutations involving βI-, βIIA-, or βIVB-tubulin isotypes with functional resemblance to βIII-tubulin in breast cancer.
P2860
A machine learned classifier that uses gene expression data to accurately predict estrogen receptor status
description
2013 nî lūn-bûn
@nan
2013 թուականի Դեկտեմբերին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի դեկտեմբերին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
A machine learned classifier t ...... edict estrogen receptor status
@ast
A machine learned classifier t ...... edict estrogen receptor status
@en
type
label
A machine learned classifier t ...... edict estrogen receptor status
@ast
A machine learned classifier t ...... edict estrogen receptor status
@en
prefLabel
A machine learned classifier t ...... edict estrogen receptor status
@ast
A machine learned classifier t ...... edict estrogen receptor status
@en
P2093
P2860
P1433
P1476
A machine learned classifier t ...... edict estrogen receptor status
@en
P2093
Jean Deschenes
Kathryn Graham
Meysam Bastani
Nasimeh Asgarian
Russell Greiner
P2860
P304
P356
10.1371/JOURNAL.PONE.0082144
P407
P577
2013-12-02T00:00:00Z