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Items: 1 to 20 of 210

1.

Prediction of drug-target interaction networks from the integration of chemical and genomic spaces.

Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M.

Bioinformatics. 2008 Jul 1;24(13):i232-40. doi: 10.1093/bioinformatics/btn162.

2.

Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework.

Yamanishi Y, Kotera M, Kanehisa M, Goto S.

Bioinformatics. 2010 Jun 15;26(12):i246-54. doi: 10.1093/bioinformatics/btq176.

3.

Supervised prediction of drug-target interactions using bipartite local models.

Bleakley K, Yamanishi Y.

Bioinformatics. 2009 Sep 15;25(18):2397-403. doi: 10.1093/bioinformatics/btp433. Epub 2009 Jul 15.

4.

[Prediction of network drug target based on improved model of bipartite graph valuation].

Liu X, Lu P, Zuo X, Chen J, Yang H, Yang Y, Gao Y.

Zhongguo Zhong Yao Za Zhi. 2012 Jan;37(2):125-9. Chinese.

PMID:
22737836
5.

Large-scale prediction of drug-target interactions using protein sequences and drug topological structures.

Cao DS, Liu S, Xu QS, Lu HM, Huang JH, Hu QN, Liang YZ.

Anal Chim Acta. 2012 Nov 8;752:1-10. doi: 10.1016/j.aca.2012.09.021. Epub 2012 Sep 24.

PMID:
23101647
6.

Using feature selection technique for drug-target interaction networks prediction.

Yu W, Jiang Z, Wang J, Tao R.

Curr Med Chem. 2011;18(36):5687-93.

PMID:
22172073
7.

Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization.

Gönen M.

Bioinformatics. 2012 Sep 15;28(18):2304-10. doi: 10.1093/bioinformatics/bts360. Epub 2012 Jun 23.

PMID:
22730431
8.

Virtual screening of GPCRs: an in silico chemogenomics approach.

Jacob L, Hoffmann B, Stoven V, Vert JP.

BMC Bioinformatics. 2008 Sep 6;9:363. doi: 10.1186/1471-2105-9-363.

9.

Drug target prediction using adverse event report systems: a pharmacogenomic approach.

Takarabe M, Kotera M, Nishimura Y, Goto S, Yamanishi Y.

Bioinformatics. 2012 Sep 15;28(18):i611-i618. doi: 10.1093/bioinformatics/bts413.

10.

Supervised enzyme network inference from the integration of genomic data and chemical information.

Yamanishi Y, Vert JP, Kanehisa M.

Bioinformatics. 2005 Jun;21 Suppl 1:i468-77.

PMID:
15961492
11.

Protein network inference from multiple genomic data: a supervised approach.

Yamanishi Y, Vert JP, Kanehisa M.

Bioinformatics. 2004 Aug 4;20 Suppl 1:i363-70.

PMID:
15262821
12.

Genome scale enzyme-metabolite and drug-target interaction predictions using the signature molecular descriptor.

Faulon JL, Misra M, Martin S, Sale K, Sapra R.

Bioinformatics. 2008 Jan 15;24(2):225-33. Epub 2007 Nov 23.

PMID:
18037612
13.

Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers.

Tabei Y, Pauwels E, Stoven V, Takemoto K, Yamanishi Y.

Bioinformatics. 2012 Sep 15;28(18):i487-i494. doi: 10.1093/bioinformatics/bts412.

14.

Kernel-based data fusion improves the drug-protein interaction prediction.

Wang YC, Zhang CH, Deng NY, Wang Y.

Comput Biol Chem. 2011 Dec 14;35(6):353-62. doi: 10.1016/j.compbiolchem.2011.10.003. Epub 2011 Oct 12.

PMID:
22099632
15.

The topology of drug-target interaction networks: implicit dependence on drug properties and target families.

Mestres J, Gregori-Puigjané E, Valverde S, Solé RV.

Mol Biosyst. 2009 Sep;5(9):1051-7. doi: 10.1039/b905821b. Epub 2009 Jul 8.

PMID:
19668871
16.

Relating drug-protein interaction network with drug side effects.

Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y.

Bioinformatics. 2012 Sep 15;28(18):i522-i528. doi: 10.1093/bioinformatics/bts383.

17.

Statistical prediction of protein chemical interactions based on chemical structure and mass spectrometry data.

Nagamine N, Sakakibara Y.

Bioinformatics. 2007 Aug 1;23(15):2004-12. Epub 2007 May 17.

PMID:
17510168
18.

Gaussian interaction profile kernels for predicting drug-target interaction.

van Laarhoven T, Nabuurs SB, Marchiori E.

Bioinformatics. 2011 Nov 1;27(21):3036-43. doi: 10.1093/bioinformatics/btr500. Epub 2011 Sep 4.

PMID:
21893517
19.

Drug-target interaction prediction by random walk on the heterogeneous network.

Chen X, Liu MX, Yan GY.

Mol Biosyst. 2012 Jul 6;8(7):1970-8. doi: 10.1039/c2mb00002d. Epub 2012 Apr 26.

PMID:
22538619
20.

DINIES: drug-target interaction network inference engine based on supervised analysis.

Yamanishi Y, Kotera M, Moriya Y, Sawada R, Kanehisa M, Goto S.

Nucleic Acids Res. 2014 Jul;42(Web Server issue):W39-45. doi: 10.1093/nar/gku337. Epub 2014 May 16.

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