145 related articles for article (PubMed ID: 26523116)
1. Soft Computing Methods for Disulfide Connectivity Prediction.
Márquez-Chamorro AE; Aguilar-Ruiz JS
Evol Bioinform Online; 2015; 11():223-9. PubMed ID: 26523116
[TBL] [Abstract][Full Text] [Related]
2. Bioinformatics approaches for disulfide connectivity prediction.
Tsai CH; Chan CH; Chen BJ; Kao CY; Liu HL; Hsu JP
Curr Protein Pept Sci; 2007 Jun; 8(3):243-60. PubMed ID: 17584119
[TBL] [Abstract][Full Text] [Related]
3. On the relevance of sophisticated structural annotations for disulfide connectivity pattern prediction.
Becker J; Maes F; Wehenkel L
PLoS One; 2013; 8(2):e56621. PubMed ID: 23533562
[TBL] [Abstract][Full Text] [Related]
4. Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.
Diwaker C; Tomar P; Poonia RC; Singh V
J Med Syst; 2018 Apr; 42(5):93. PubMed ID: 29637392
[TBL] [Abstract][Full Text] [Related]
5. Role of Soft Computing Approaches in HealthCare Domain: A Mini Review.
Gambhir S; Malik SK; Kumar Y
J Med Syst; 2016 Dec; 40(12):287. PubMed ID: 27796841
[TBL] [Abstract][Full Text] [Related]
6. Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.
Song J; Yuan Z; Tan H; Huber T; Burrage K
Bioinformatics; 2007 Dec; 23(23):3147-54. PubMed ID: 17942444
[TBL] [Abstract][Full Text] [Related]
7. Disulfide connectivity prediction using recursive neural networks and evolutionary information.
Vullo A; Frasconi P
Bioinformatics; 2004 Mar; 20(5):653-9. PubMed ID: 15033872
[TBL] [Abstract][Full Text] [Related]
8. Disulfide connectivity prediction based on structural information without a prior knowledge of the bonding state of cysteines.
Lin HH; Hsu JC; Hsu YN; Pan RH; Chen YF; Tseng LY
Comput Biol Med; 2013 Nov; 43(11):1941-8. PubMed ID: 24209939
[TBL] [Abstract][Full Text] [Related]
9. Cysteine separations profiles on protein sequences infer disulfide connectivity.
Zhao E; Liu HL; Tsai CH; Tsai HK; Chan CH; Kao CY
Bioinformatics; 2005 Apr; 21(8):1415-20. PubMed ID: 15585533
[TBL] [Abstract][Full Text] [Related]
10. Predicting disulfide connectivity patterns.
Lu CH; Chen YC; Yu CS; Hwang JK
Proteins; 2007 May; 67(2):262-70. PubMed ID: 17285623
[TBL] [Abstract][Full Text] [Related]
11. Identifying cysteines and histidines in transition-metal-binding sites using support vector machines and neural networks.
Passerini A; Punta M; Ceroni A; Rost B; Frasconi P
Proteins; 2006 Nov; 65(2):305-16. PubMed ID: 16927295
[TBL] [Abstract][Full Text] [Related]
12. A simplified approach to disulfide connectivity prediction from protein sequences.
Vincent M; Passerini A; Labbé M; Frasconi P
BMC Bioinformatics; 2008 Jan; 9():20. PubMed ID: 18194539
[TBL] [Abstract][Full Text] [Related]
13. The role of soft computing in intelligent machines.
de Silva CW
Philos Trans A Math Phys Eng Sci; 2003 Aug; 361(1809):1749-80. PubMed ID: 12952684
[TBL] [Abstract][Full Text] [Related]
14. Inter- and intra-chain disulfide bond prediction based on optimal feature selection.
Niu S; Huang T; Feng KY; He Z; Cui W; Gu L; Li H; Cai YD; Li Y
Protein Pept Lett; 2013 Mar; 20(3):324-35. PubMed ID: 22591475
[TBL] [Abstract][Full Text] [Related]
15. Improving disulfide connectivity prediction with sequential distance between oxidized cysteines.
Tsai CH; Chen BJ; Chan CH; Liu HL; Kao CY
Bioinformatics; 2005 Dec; 21(24):4416-9. PubMed ID: 16223789
[TBL] [Abstract][Full Text] [Related]
16. Prediction of disulfide bond engineering sites using a machine learning method.
Gao X; Dong X; Li X; Liu Z; Liu H
Sci Rep; 2020 Jun; 10(1):10330. PubMed ID: 32587353
[TBL] [Abstract][Full Text] [Related]
17. Predicting disulfide bond connectivity in proteins by correlated mutations analysis.
Rubinstein R; Fiser A
Bioinformatics; 2008 Feb; 24(4):498-504. PubMed ID: 18203772
[TBL] [Abstract][Full Text] [Related]
18. Predicting the state of cysteines based on sequence information.
Guang X; Guo Y; Xiao J; Wang X; Sun J; Xiong W; Li M
J Theor Biol; 2010 Dec; 267(3):312-8. PubMed ID: 20826168
[TBL] [Abstract][Full Text] [Related]
19. Prediction of disulfide connectivity from protein sequences.
Chen YC; Hwang JK
Proteins; 2005 Nov; 61(3):507-12. PubMed ID: 16170781
[TBL] [Abstract][Full Text] [Related]
20. Accurate disulfide-bonding network predictions improve ab initio structure prediction of cysteine-rich proteins.
Yang J; He BJ; Jang R; Zhang Y; Shen HB
Bioinformatics; 2015 Dec; 31(23):3773-81. PubMed ID: 26254435
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
