1101 related articles for article (PubMed ID: 26415726)
1. Computational probing protein-protein interactions targeting small molecules.
Wang YC; Chen SL; Deng NY; Wang Y
Bioinformatics; 2016 Jan; 32(2):226-34. PubMed ID: 26415726
[TBL] [Abstract][Full Text] [Related]
2. Network predicting drug's anatomical therapeutic chemical code.
Wang YC; Chen SL; Deng NY; Wang Y
Bioinformatics; 2013 May; 29(10):1317-24. PubMed ID: 23564845
[TBL] [Abstract][Full Text] [Related]
3. Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder.
Fu Y; Guo Y; Wang Y; Luo J; Pu X; Li M; Zhang Z
Comput Biol Chem; 2015 Jun; 56():41-8. PubMed ID: 25854804
[TBL] [Abstract][Full Text] [Related]
4. Kernel-based data fusion improves the drug-protein interaction prediction.
Wang YC; Zhang CH; Deng NY; Wang Y
Comput Biol Chem; 2011 Dec; 35(6):353-62. PubMed ID: 22099632
[TBL] [Abstract][Full Text] [Related]
5. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences.
An JY; You ZH; Meng FR; Xu SJ; Wang Y
Int J Mol Sci; 2016 May; 17(5):. PubMed ID: 27213337
[TBL] [Abstract][Full Text] [Related]
6. DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.
Yuan Q; Gao J; Wu D; Zhang S; Mamitsuka H; Zhu S
Bioinformatics; 2016 Jun; 32(12):i18-i27. PubMed ID: 27307615
[TBL] [Abstract][Full Text] [Related]
7. Assessing the druggability of protein-protein interactions by a supervised machine-learning method.
Sugaya N; Ikeda K
BMC Bioinformatics; 2009 Aug; 10():263. PubMed ID: 19703312
[TBL] [Abstract][Full Text] [Related]
8. Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set.
You ZH; Zhu L; Zheng CH; Yu HJ; Deng SP; Ji Z
BMC Bioinformatics; 2014; 15 Suppl 15(Suppl 15):S9. PubMed ID: 25474679
[TBL] [Abstract][Full Text] [Related]
9. Similarity-based prediction for Anatomical Therapeutic Chemical classification of drugs by integrating multiple data sources.
Liu Z; Guo F; Gu J; Wang Y; Li Y; Wang D; Lu L; Li D; He F
Bioinformatics; 2015 Jun; 31(11):1788-95. PubMed ID: 25638810
[TBL] [Abstract][Full Text] [Related]
10. NCC-AUC: an AUC optimization method to identify multi-biomarker panel for cancer prognosis from genomic and clinical data.
Zou M; Liu Z; Zhang XS; Wang Y
Bioinformatics; 2015 Oct; 31(20):3330-8. PubMed ID: 26092859
[TBL] [Abstract][Full Text] [Related]
11. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier.
Li ZW; You ZH; Chen X; Li LP; Huang DS; Yan GY; Nie R; Huang YA
Oncotarget; 2017 Apr; 8(14):23638-23649. PubMed ID: 28423569
[TBL] [Abstract][Full Text] [Related]
12. An integrative in silico approach for discovering candidates for drug-targetable protein-protein interactions in interactome data.
Sugaya N; Ikeda K; Tashiro T; Takeda S; Otomo J; Ishida Y; Shiratori A; Toyoda A; Noguchi H; Takeda T; Kuhara S; Sakaki Y; Iwayanagi T
BMC Pharmacol; 2007 Aug; 7():10. PubMed ID: 17705877
[TBL] [Abstract][Full Text] [Related]
13. Identifying Drug-Target Interactions with Decision Templates.
Yan XY; Zhang SW
Curr Protein Pept Sci; 2018; 19(5):498-506. PubMed ID: 27829344
[TBL] [Abstract][Full Text] [Related]
14. Comparative analysis and assessment of M. tuberculosis H37Rv protein-protein interaction datasets.
Zhou H; Wong L
BMC Genomics; 2011 Nov; 12 Suppl 3(Suppl 3):S20. PubMed ID: 22369691
[TBL] [Abstract][Full Text] [Related]
15. Effectively Identifying Compound-Protein Interactions by Learning from Positive and Unlabeled Examples.
Cheng Z; Zhou S; Wang Y; Liu H; Guan J; Chen YP
IEEE/ACM Trans Comput Biol Bioinform; 2018; 15(6):1832-1843. PubMed ID: 28113437
[TBL] [Abstract][Full Text] [Related]
16. MEGADOCK-Web: an integrated database of high-throughput structure-based protein-protein interaction predictions.
Hayashi T; Matsuzaki Y; Yanagisawa K; Ohue M; Akiyama Y
BMC Bioinformatics; 2018 May; 19(Suppl 4):62. PubMed ID: 29745830
[TBL] [Abstract][Full Text] [Related]
17. Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening.
Nagamine N; Shirakawa T; Minato Y; Torii K; Kobayashi H; Imoto M; Sakakibara Y
PLoS Comput Biol; 2009 Jun; 5(6):e1000397. PubMed ID: 19503826
[TBL] [Abstract][Full Text] [Related]
18. Improving protein-protein interactions prediction accuracy using protein evolutionary information and relevance vector machine model.
An JY; Meng FR; You ZH; Chen X; Yan GY; Hu JP
Protein Sci; 2016 Oct; 25(10):1825-33. PubMed ID: 27452983
[TBL] [Abstract][Full Text] [Related]
19. A Cascade Random Forests Algorithm for Predicting Protein-Protein Interaction Sites.
Wei ZS; Yang JY; Shen HB; Yu DJ
IEEE Trans Nanobioscience; 2015 Oct; 14(7):746-60. PubMed ID: 26441427
[TBL] [Abstract][Full Text] [Related]
20. Prediction of protein-RNA binding sites by a random forest method with combined features.
Liu ZP; Wu LY; Wang Y; Zhang XS; Chen L
Bioinformatics; 2010 Jul; 26(13):1616-22. PubMed ID: 20483814
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
