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Items: 1 to 20 of 105

1.

Incorporating amino acids composition and functional domains for identifying bacterial toxin proteins.

Su MG, Huang CH, Lee TY, Chen YJ, Wu HY.

Biomed Res Int. 2014;2014:972692. doi: 10.1155/2014/972692.

2.

BTXpred: prediction of bacterial toxins.

Saha S, Raghava GP.

In Silico Biol. 2007;7(4-5):405-12.

PMID:
18391233
3.

Incorporating evolutionary information and functional domains for identifying RNA splicing factors in humans.

Hsu JB, BretaƱa NA, Lee TY, Huang HD.

PLoS One. 2011;6(11):e27567. doi: 10.1371/journal.pone.0027567.

4.

DBETH: a Database of Bacterial Exotoxins for Human.

Chakraborty A, Ghosh S, Chowdhary G, Maulik U, Chakrabarti S.

Nucleic Acids Res. 2012 Jan;40(Database issue):D615-20. doi: 10.1093/nar/gkr942.

5.
6.

Incorporating significant amino acid pairs and protein domains to predict RNA splicing-related proteins with functional roles.

Hsu JB, Huang KY, Weng TY, Huang CH, Lee TY.

J Comput Aided Mol Des. 2014 Jan;28(1):49-60. doi: 10.1007/s10822-014-9706-6.

PMID:
24442949
7.

Crystallization and preliminary crystallographic study of the functional form of the Bacillus thuringiensis mosquito-larvicidal Cry4Aa mutant toxin.

Boonserm P, Angsuthanasombat C, Lescar J.

Acta Crystallogr D Biol Crystallogr. 2004 Jul;60(Pt 7):1315-8.

PMID:
15213403
8.

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.

9.

Purification of bacterial exotoxins. The case of botulinum, tetanus, anthrax, pertussis and cholera toxins.

Pasechnik VA, Shone CC, Hambleton P.

Bioseparation. 1992-1993;3(5):267-83. Review.

PMID:
1369426
10.

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.

12.

An intelligent system for identifying acetylated lysine on histones and nonhistone proteins.

Lu CT, Lee TY, Chen YJ, Chen YJ.

Biomed Res Int. 2014;2014:528650. doi: 10.1155/2014/528650.

13.

High level of soluble expression in Escherichia coli and characterisation of the cloned Bacillus thuringiensis Cry4Ba domain III fragment.

Chayaratanasin P, Moonsom S, Sakdee S, Chaisri U, Katzenmeier G, Angsuthanasombat C.

J Biochem Mol Biol. 2007 Jan 31;40(1):58-64.

PMID:
17244483
14.

Prediction of nuclear receptors with optimal pseudo amino acid composition.

Gao QB, Jin ZC, Ye XF, Wu C, He J.

Anal Biochem. 2009 Apr 1;387(1):54-9. doi: 10.1016/j.ab.2009.01.018.

PMID:
19454254
15.

Identification of voltage-gated potassium channel subfamilies from sequence information using support vector machine.

Chen W, Lin H.

Comput Biol Med. 2012 Apr;42(4):504-7. doi: 10.1016/j.compbiomed.2012.01.003.

PMID:
22297432
16.

Prediction and classification of aminoacyl tRNA synthetases using PROSITE domains.

Panwar B, Raghava GP.

BMC Genomics. 2010 Sep 22;11:507. doi: 10.1186/1471-2164-11-507.

17.
18.

Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition.

Hayat M, Khan A.

J Theor Biol. 2011 Feb 21;271(1):10-7. doi: 10.1016/j.jtbi.2010.11.017.

PMID:
21110985
19.

Redesigning Bacillus thuringiensis Cry1Aa toxin into a mosquito toxin.

Liu XS, Dean DH.

Protein Eng Des Sel. 2006 Mar;19(3):107-11.

PMID:
16436453
20.

Classification of membrane protein types using Voting Feature Interval in combination with Chou's Pseudo Amino Acid Composition.

Ali F, Hayat M.

J Theor Biol. 2015 Nov 7;384:78-83. doi: 10.1016/j.jtbi.2015.07.034.

PMID:
26297889

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