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https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1852326/
Here we describe a method that uses support vector machines together with a large set of informative features to improve contact map prediction. On the same data set, SVMcon outperforms the latest version of the CMAPpro contact map predictor [28,35] and is ranked as one of the top predictors in the blind and independent CASP7 experiment.
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http://calla.rnet.missouri.edu/cheng_courses/svmcon_accepted.pdf
Improved Residue Contact Prediction using Support Vector Machines and a Large Feature Set Jianlin Cheng∗1, Pierre Baldi2 1School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA 2School of Information and Computer Sciences, University of California Irvine, Irvine, CA 92617, USA
https://link.springer.com/article/10.1186%2F1471-2105-8-113
Improved residue contact prediction using support vector machines and a large feature set. Authors; Authors and affiliations; ... we describe a method that uses support vector machines together with a large set of informative features to improve contact map prediction. On the same data set, ...Cited by: 244
https://www.ncbi.nlm.nih.gov/pubmed/17407573
Apr 02, 2007 · Improved residue contact prediction using support vector machines and a large feature set. Cheng J(1), Baldi P. Author information: (1)School of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816-2362, USA. [email protected] by: 244
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