Progress and challenges in the computational prediction of gene function using networks.
about
Characterizing the state of the art in the computational assignment of gene function: lessons from the first critical assessment of functional annotation (CAFA)Text mining in cancer gene and pathway prioritization.Parametric Bayesian priors and better choice of negative examples improve protein function prediction.Progress and challenges in the computational prediction of gene function using networks: 2012-2013 updateCommWalker: Correctly Evaluating Modules in Molecular Networks in Light of Annotation Bias.Exploration of gene functions for esophageal squamous cell carcinoma using network-based guilt by association principle.
P2860
Progress and challenges in the computational prediction of gene function using networks.
description
2012 nî lūn-bûn
@nan
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
2012年论文
@zh
2012年论文
@zh-cn
name
Progress and challenges in the computational prediction of gene function using networks.
@en
Progress and challenges in the computational prediction of gene function using networks.
@nl
type
label
Progress and challenges in the computational prediction of gene function using networks.
@en
Progress and challenges in the computational prediction of gene function using networks.
@nl
prefLabel
Progress and challenges in the computational prediction of gene function using networks.
@en
Progress and challenges in the computational prediction of gene function using networks.
@nl
P2860
P1433
P1476
Progress and challenges in the computational prediction of gene function using networks.
@en
P2860
P356
10.12688/F1000RESEARCH.1-14.V1
P577
2012-09-07T00:00:00Z