202 related articles for article (PubMed ID: 15963230)
1. pSLIP: SVM based protein subcellular localization prediction using multiple physicochemical properties.
Sarda D; Chua GH; Li KB; Krishnan A
BMC Bioinformatics; 2005 Jun; 6():152. PubMed ID: 15963230
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. Subcellular localization prediction with new protein encoding schemes.
Oğul H; Mumcuoğu EU
IEEE/ACM Trans Comput Biol Bioinform; 2007; 4(2):227-32. PubMed ID: 17473316
[TBL] [Abstract][Full Text] [Related]
4. PSLpred: prediction of subcellular localization of bacterial proteins.
Bhasin M; Garg A; Raghava GP
Bioinformatics; 2005 May; 21(10):2522-4. PubMed ID: 15699023
[TBL] [Abstract][Full Text] [Related]
5. Support vector machine approach for protein subcellular localization prediction.
Hua S; Sun Z
Bioinformatics; 2001 Aug; 17(8):721-8. PubMed ID: 11524373
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. A novel approach for protein subcellular location prediction using amino acid exposure.
Mer AS; Andrade-Navarro MA
BMC Bioinformatics; 2013 Nov; 14():342. PubMed ID: 24283794
[TBL] [Abstract][Full Text] [Related]
10. Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method.
Yao YH; Lv YP; Li L; Xu HM; Ji BB; Chen J; Li C; Liao B; Nan XY
BMC Bioinformatics; 2019 Dec; 20(Suppl 22):719. PubMed ID: 31888447
[TBL] [Abstract][Full Text] [Related]
11. Prediction of protein subcellular localization based on variable-length motifs detection and dissimilarity based classification.
Arango-Argoty GA; Jaramillo-Garzón JA; Röthlisberger S; Castellanos-Dominguez CG
Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():945-8. PubMed ID: 22254467
[TBL] [Abstract][Full Text] [Related]
12. AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices.
Tantoso E; Li KB
Amino Acids; 2008 Aug; 35(2):345-53. PubMed ID: 18163182
[TBL] [Abstract][Full Text] [Related]
13. Prediction of protein subcellular locations using a new measure of information discrepancy.
Jin L; Tang H; Fang W
J Bioinform Comput Biol; 2005 Aug; 3(4):915-27. PubMed ID: 16078367
[TBL] [Abstract][Full Text] [Related]
14. Predicting subcellular localization with AdaBoost Learner.
Jin YH; Niu B; Feng KY; Lu WC; Cai YD; Li GZ
Protein Pept Lett; 2008; 15(3):286-9. PubMed ID: 18336359
[TBL] [Abstract][Full Text] [Related]
15. SherLoc: high-accuracy prediction of protein subcellular localization by integrating text and protein sequence data.
Shatkay H; Höglund A; Brady S; Blum T; Dönnes P; Kohlbacher O
Bioinformatics; 2007 Jun; 23(11):1410-7. PubMed ID: 17392328
[TBL] [Abstract][Full Text] [Related]
16. A novel method for predicting protein subcellular localization based on pseudo amino acid composition.
Ma J; Gu H
BMB Rep; 2010 Oct; 43(10):670-6. PubMed ID: 21034529
[TBL] [Abstract][Full Text] [Related]
17. SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition.
Melvin I; Ie E; Kuang R; Weston J; Stafford WN; Leslie C
BMC Bioinformatics; 2007 May; 8 Suppl 4(Suppl 4):S2. PubMed ID: 17570145
[TBL] [Abstract][Full Text] [Related]
18. Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.
Bouziane H; Chouarfia A
J Integr Bioinform; 2020 Jun; 18(1):51-79. PubMed ID: 32598314
[TBL] [Abstract][Full Text] [Related]
19. Prediction of nuclear proteins using nuclear translocation signals proposed by probabilistic latent semantic indexing.
Su EC; Chang JM; Cheng CW; Sung TY; Hsu WL
BMC Bioinformatics; 2012; 13 Suppl 17(Suppl 17):S13. PubMed ID: 23282098
[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]