Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
about
Time, space, and disorder in the expanding proteome universe.Using hyperLOPIT to perform high-resolution mapping of the spatial proteome.Compositional Dynamics: Defining the Fuzzy Cell.Correlation profiling of brain sub-cellular proteomes reveals co-assembly of synaptic proteins and subcellular distribution.A Bioconductor workflow for processing and analysing spatial proteomics data
P2860
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
description
2016 nî lūn-bûn
@nan
2016 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2016 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2016年の論文
@ja
2016年論文
@yue
2016年論文
@zh-hant
2016年論文
@zh-hk
2016年論文
@zh-mo
2016年論文
@zh-tw
2016年论文
@wuu
name
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@ast
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@en
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@nl
type
label
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@ast
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@en
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@nl
prefLabel
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@ast
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@en
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@nl
P2093
P2860
P50
P3181
P1476
Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics
@en
P2093
Andy Christoforou
Arnoud Groen
Kathryn S Lilley
Lisa M Breckels
Matthew W B Trotter
Sean B Holden
P2860
P304
P3181
P356
10.1371/JOURNAL.PCBI.1004920
P407
P577
2016-05-01T00:00:00Z