A robust and accurate method for feature selection and prioritization from multi-class OMICs data.
about
Integration of metabolomics, lipidomics and clinical data using a machine learning method.Multi-class computational evolution: development, benchmark evaluation and application to RNA-Seq biomarker discovery.Tensor decomposition-based unsupervised feature extraction applied to matrix products for multi-view data processing.Seeing the wood for the trees: a forest of methods for optimization and omic-network integration in metabolic modelling.
P2860
A robust and accurate method for feature selection and prioritization from multi-class OMICs data.
description
2014 nî lūn-bûn
@nan
2014 թուականի Սեպտեմբերին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի սեպտեմբերին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
A robust and accurate method f ...... n from multi-class OMICs data.
@ast
A robust and accurate method f ...... n from multi-class OMICs data.
@en
type
label
A robust and accurate method f ...... n from multi-class OMICs data.
@ast
A robust and accurate method f ...... n from multi-class OMICs data.
@en
prefLabel
A robust and accurate method f ...... n from multi-class OMICs data.
@ast
A robust and accurate method f ...... n from multi-class OMICs data.
@en
P2860
P50
P1433
P1476
A robust and accurate method f ...... n from multi-class OMICs data.
@en
P2093
Vittorio Fortino
P2860
P304
P356
10.1371/JOURNAL.PONE.0107801
P407
P577
2014-09-23T00:00:00Z