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Items: 1 to 20 of 141

1.

Prediction and analysis of protein solubility using a novel scoring card method with dipeptide composition.

Huang HL, Charoenkwan P, Kao TF, Lee HC, Chang FL, Huang WL, Ho SJ, Shu LS, Chen WL, Ho SY.

BMC Bioinformatics. 2012;13 Suppl 17:S3. doi: 10.1186/1471-2105-13-S17-S3. Epub 2012 Dec 13.

2.

SCMCRYS: predicting protein crystallization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs.

Charoenkwan P, Shoombuatong W, Lee HC, Chaijaruwanich J, Huang HL, Ho SY.

PLoS One. 2013 Sep 3;8(9):e72368. doi: 10.1371/journal.pone.0072368. eCollection 2013.

3.

SCMMTP: identifying and characterizing membrane transport proteins using propensity scores of dipeptides.

Liou YF, Vasylenko T, Yeh CL, Lin WC, Chiu SH, Charoenkwan P, Shu LS, Ho SY, Huang HL.

BMC Genomics. 2015;16 Suppl 12:S6. doi: 10.1186/1471-2164-16-S12-S6. Epub 2015 Dec 9.

4.

SCMPSP: Prediction and characterization of photosynthetic proteins based on a scoring card method.

Vasylenko T, Liou YF, Chen HA, Charoenkwan P, Huang HL, Ho SY.

BMC Bioinformatics. 2015;16 Suppl 1:S8. doi: 10.1186/1471-2105-16-S1-S8. Epub 2015 Jan 21.

5.

SCMHBP: prediction and analysis of heme binding proteins using propensity scores of dipeptides.

Liou YF, Charoenkwan P, Srinivasulu Y, Vasylenko T, Lai SC, Lee HC, Chen YH, Huang HL, Ho SY.

BMC Bioinformatics. 2014;15 Suppl 16:S4. doi: 10.1186/1471-2105-15-S16-S4. Epub 2014 Dec 8.

6.

Interconnection between the protein solubility and amino acid and dipeptide compositions.

Niu X, Li N, Chen D, Wang Z.

Protein Pept Lett. 2013 Jan;20(1):88-95.

PMID:
22789104
7.

Propensity scores for prediction and characterization of bioluminescent proteins from sequences.

Huang HL.

PLoS One. 2014 May 14;9(5):e97158. doi: 10.1371/journal.pone.0097158. eCollection 2014.

8.

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:S13. doi: 10.1186/1471-2105-13-S17-S13. Epub 2012 Dec 13.

9.

Identification and characterization of plastid-type proteins from sequence-attributed features using machine learning.

Kaundal R, Sahu SS, Verma R, Weirick T.

BMC Bioinformatics. 2013;14 Suppl 14:S7. doi: 10.1186/1471-2105-14-S14-S7. Epub 2013 Oct 9.

10.

Detecting thermophilic proteins through selecting amino acid and dipeptide composition features.

Nakariyakul S, Liu ZP, Chen L.

Amino Acids. 2012 May;42(5):1947-53. doi: 10.1007/s00726-011-0923-1. Epub 2011 May 6.

PMID:
21547362
11.

Prediction and analysis of antibody amyloidogenesis from sequences.

Liaw C, Tung CW, Ho SY.

PLoS One. 2013;8(1):e53235. doi: 10.1371/journal.pone.0053235. Epub 2013 Jan 7.

12.

SCMBYK: prediction and characterization of bacterial tyrosine-kinases based on propensity scores of dipeptides.

Vasylenko T, Liou YF, Chiou PC, Chu HW, Lai YS, Chou YL, Huang HL, Ho SY.

BMC Bioinformatics. 2016 Dec 22;17(Suppl 19):514. doi: 10.1186/s12859-016-1371-4.

13.

Support vector machine (SVM) based multiclass prediction with basic statistical analysis of plasminogen activators.

Muthukrishnan S, Puri M, Lefevre C.

BMC Res Notes. 2014 Jan 27;7:63. doi: 10.1186/1756-0500-7-63.

14.

Computational identification of ubiquitylation sites from protein sequences.

Tung CW, Ho SY.

BMC Bioinformatics. 2008 Jul 15;9:310. doi: 10.1186/1471-2105-9-310.

15.

Ranking Gene Ontology terms for predicting non-classical secretory proteins in eukaryotes and prokaryotes.

Huang WL.

J Theor Biol. 2012 Nov 7;312:105-13. doi: 10.1016/j.jtbi.2012.07.027. Epub 2012 Aug 8.

16.

Combing ontologies and dipeptide composition for predicting DNA-binding proteins.

Nanni L, Lumini A.

Amino Acids. 2008 May;34(4):635-41. doi: 10.1007/s00726-007-0016-3. Epub 2008 Jan 4.

PMID:
18175049
17.

Prediction of Protein Submitochondrial Locations by Incorporating Dipeptide Composition into Chou's General Pseudo Amino Acid Composition.

Ahmad K, Waris M, Hayat M.

J Membr Biol. 2016 Jun;249(3):293-304. doi: 10.1007/s00232-015-9868-8. Epub 2016 Jan 8.

PMID:
26746980
18.

Prediction of mitochondrial proteins based on genetic algorithm - partial least squares and support vector machine.

Tan F, Feng X, Fang Z, Li M, Guo Y, Jiang L.

Amino Acids. 2007 Nov;33(4):669-75. Epub 2007 Aug 15.

PMID:
17701100
19.

A support vector machine-based method for predicting the propensity of a protein to be soluble or to form inclusion body on overexpression in Escherichia coli.

Idicula-Thomas S, Kulkarni AJ, Kulkarni BD, Jayaraman VK, Balaji PV.

Bioinformatics. 2006 Feb 1;22(3):278-84. Epub 2005 Dec 6.

PMID:
16332713
20.

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