Predicting existing targets for new drugs base on strategies for missing interactions.
about
Predicting binary, discrete and continued lncRNA-disease associations via a unified framework based on graph regression.Predicting and understanding comprehensive drug-drug interactions via semi-nonnegative matrix factorization.A two-tiered unsupervised clustering approach for drug repositioning through heterogeneous data integration.BMCMDA: a novel model for predicting human microbe-disease associations via binary matrix completionAn Integrated Local Classification Model of Predicting Drug-Drug Interactions via Dempster-Shafer Theory of Evidence
P2860
Predicting existing targets for new drugs base on strategies for missing interactions.
description
2016 nî lūn-bûn
@nan
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
2016年论文
@zh
2016年论文
@zh-cn
name
Predicting existing targets for new drugs base on strategies for missing interactions.
@ast
Predicting existing targets for new drugs base on strategies for missing interactions.
@en
type
label
Predicting existing targets for new drugs base on strategies for missing interactions.
@ast
Predicting existing targets for new drugs base on strategies for missing interactions.
@en
prefLabel
Predicting existing targets for new drugs base on strategies for missing interactions.
@ast
Predicting existing targets for new drugs base on strategies for missing interactions.
@en
P2860
P1433
P1476
Predicting existing targets for new drugs base on strategies for missing interactions.
@en
P2093
Jia-Xin Li
P2860
P2888
P356
10.1186/S12859-016-1118-2
P478
17 Suppl 8
P577
2016-08-31T00:00:00Z
P6179
1001833764