iPcc: a novel feature extraction method for accurate disease class discovery and prediction.
about
Integrating heterogeneous genomic data to accurately identify disease subtypesMachine learning applications in cancer prognosis and predictionUnravelling personalized dysfunctional gene network of complex diseases based on differential network modelRandom Subspace Aggregation for Cancer Prediction with Gene Expression Profiles
P2860
iPcc: a novel feature extraction method for accurate disease class discovery and prediction.
description
article científic
@ca
article scientifique
@fr
articolo scientifico
@it
artigo científico
@pt
bilimsel makale
@tr
scientific article published on 12 June 2013
@en
vedecký článok
@sk
vetenskaplig artikel
@sv
videnskabelig artikel
@da
vědecký článek
@cs
name
iPcc: a novel feature extracti ...... lass discovery and prediction.
@en
iPcc: a novel feature extracti ...... lass discovery and prediction.
@nl
type
label
iPcc: a novel feature extracti ...... lass discovery and prediction.
@en
iPcc: a novel feature extracti ...... lass discovery and prediction.
@nl
prefLabel
iPcc: a novel feature extracti ...... lass discovery and prediction.
@en
iPcc: a novel feature extracti ...... lass discovery and prediction.
@nl
P2093
P2860
P356
P1476
iPcc: a novel feature extracti ...... lass discovery and prediction.
@en
P2093
Xiang-Sun Zhang
Xianwen Ren
P2860
P356
10.1093/NAR/GKT343
P407
P577
2013-06-12T00:00:00Z