147 related articles for article (PubMed ID: 15759622)
1. Go molecular function terms are predictive of subcellular localization.
Lu Z; Hunter L
Pac Symp Biocomput; 2005; ():151-61. PubMed ID: 15759622
[TBL] [Abstract][Full Text] [Related]
2. ProLoc-GO: utilizing informative Gene Ontology terms for sequence-based prediction of protein subcellular localization.
Huang WL; Tung CW; Ho SW; Hwang SF; Ho SY
BMC Bioinformatics; 2008 Feb; 9():80. PubMed ID: 18241343
[TBL] [Abstract][Full Text] [Related]
3. Gene ontology based transfer learning for protein subcellular localization.
Mei S; Fei W; Zhou S
BMC Bioinformatics; 2011 Feb; 12():44. PubMed ID: 21284890
[TBL] [Abstract][Full Text] [Related]
4. Hum-PLoc: a novel ensemble classifier for predicting human protein subcellular localization.
Chou KC; Shen HB
Biochem Biophys Res Commun; 2006 Aug; 347(1):150-7. PubMed ID: 16808903
[TBL] [Abstract][Full Text] [Related]
5. Prediction of protein subcellular locations by GO-FunD-PseAA predictor.
Chou KC; Cai YD
Biochem Biophys Res Commun; 2004 Aug; 320(4):1236-9. PubMed ID: 15249222
[TBL] [Abstract][Full Text] [Related]
6. Ranking Gene Ontology terms for predicting non-classical secretory proteins in eukaryotes and prokaryotes.
Huang WL
J Theor Biol; 2012 Nov; 312():105-13. PubMed ID: 22967952
[TBL] [Abstract][Full Text] [Related]
7. mLASSO-Hum: A LASSO-based interpretable human-protein subcellular localization predictor.
Wan S; Mak MW; Kung SY
J Theor Biol; 2015 Oct; 382():223-34. PubMed ID: 26164062
[TBL] [Abstract][Full Text] [Related]
8. Protein subcellular localization prediction using artificial intelligence technology.
Nair R; Rost B
Methods Mol Biol; 2008; 484():435-63. PubMed ID: 18592195
[TBL] [Abstract][Full Text] [Related]
9. Prediction of protein subcellular locations by support vector machines using compositions of amino acids and amino acid pairs.
Park KJ; Kanehisa M
Bioinformatics; 2003 Sep; 19(13):1656-63. PubMed ID: 12967962
[TBL] [Abstract][Full Text] [Related]
10. Prediction of protein subcellular localization.
Yu CS; Chen YC; Lu CH; Hwang JK
Proteins; 2006 Aug; 64(3):643-51. PubMed ID: 16752418
[TBL] [Abstract][Full Text] [Related]
11. Improving subcellular localization prediction using text classification and the gene ontology.
Fyshe A; Liu Y; Szafron D; Greiner R; Lu P
Bioinformatics; 2008 Nov; 24(21):2512-7. PubMed ID: 18728042
[TBL] [Abstract][Full Text] [Related]
12. Predicting protein subcellular localization based on information content of gene ontology terms.
Zhang SB; Tang QR
Comput Biol Chem; 2016 Dec; 65():1-7. PubMed ID: 27665466
[TBL] [Abstract][Full Text] [Related]
13. GOASVM: a subcellular location predictor by incorporating term-frequency gene ontology into the general form of Chou's pseudo-amino acid composition.
Wan S; Mak MW; Kung SY
J Theor Biol; 2013 Apr; 323():40-8. PubMed ID: 23376577
[TBL] [Abstract][Full Text] [Related]
14. WegoLoc: accurate prediction of protein subcellular localization using weighted Gene Ontology terms.
Chi SM; Nam D
Bioinformatics; 2012 Apr; 28(7):1028-30. PubMed ID: 22296788
[TBL] [Abstract][Full Text] [Related]
15. HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.
Wan S; Mak MW; Kung SY
PLoS One; 2014; 9(3):e89545. PubMed ID: 24647341
[TBL] [Abstract][Full Text] [Related]
16. iLoc-Hum: using the accumulation-label scale to predict subcellular locations of human proteins with both single and multiple sites.
Chou KC; Wu ZC; Xiao X
Mol Biosyst; 2012 Feb; 8(2):629-41. PubMed ID: 22134333
[TBL] [Abstract][Full Text] [Related]
17. Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.
Wan S; Mak MW; Kung SY
BMC Bioinformatics; 2016 Feb; 17():97. PubMed ID: 26911432
[TBL] [Abstract][Full Text] [Related]
18. MSLoc-DT: a new method for predicting the protein subcellular location of multispecies based on decision templates.
Zhang SW; Liu YF; Yu Y; Zhang TH; Fan XN
Anal Biochem; 2014 Mar; 449():164-71. PubMed ID: 24361712
[TBL] [Abstract][Full Text] [Related]
19. Supervised learning method for the prediction of subcellular localization of proteins using amino acid and amino acid pair composition.
Habib T; Zhang C; Yang JY; Yang MQ; Deng Y
BMC Genomics; 2008; 9 Suppl 1(Suppl 1):S16. PubMed ID: 18366605
[TBL] [Abstract][Full Text] [Related]
20. Gram-positive and Gram-negative subcellular localization using rotation forest and physicochemical-based features.
Dehzangi A; Sohrabi S; Heffernan R; Sharma A; Lyons J; Paliwal K; Sattar A
BMC Bioinformatics; 2015; 16 Suppl 4(Suppl 4):S1. PubMed ID: 25734546
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]