Support vector machine modeling

Norinder, U. (2003) Support vector machine models in drug design -applications to drug transport processes and QSAR using simplex optimisations and variable selection. Neurocomputing, 55, 337-346. 

Xu, C., Dai, F.C., Xu, X.W., Lee, Y.H. 2012a. GIS-based support vector machine modeling of earthquake-triggered landslide susceptibility in the Jianjiang River watershed, China. Geomorphology 145-146 70-80. 

Chauchard, F. et aL (2005) Least-squares support vector machines modelization for time-resolved spectroscopy. Appl. Opt., 44 (33), 7091-7097. 

Jorissen RN, Gilson MK. Virtual screening of molecular databases using a Support Vector Machine. J Chem Inf Model 2005 45 549-61. 

A variety of other QSAR-type models for the prediction of plasma protein binding have also been published recently, including neural networks/support vector machines [64], 4-D fingerprints [65], and TOPS-MODE descriptors [66]. 

Tobita, M., Nishikawa, T. and Nagashima, R. (2005) A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors. Bioorganic el Medicinal Chemistry Letters, 15, 2886-2890. 

Huanxiang L, Xiaojun Y, Ruisheng Zh, Mancang L, Zhide H, Botao F (2005) Accurate quantitative structure-property relationship model to predict the solubility of C60 in various solvents based on a novel approach using a least-squares support vector machine. J. Phys. Chem. Sect B. 109 20565-20571. 

Multivariate models using neural networks, support vector machines and least median squares regression have been used to predict hERG activity [96-98]. These types of models function more as computational black box assays. 

Histone deacetylases (HDACs) play a critical role in transcription regulation. Small molecule HDAC inhibitors have become an emerging target for the treatment of cancer and other cell proliferation diseases. We have employed variable selection k nearest neighbor approach (iNN)and support vector machines (SVM) approach to generate QSAR models for 59 chemically diverse... 

Support Vector Machine (SVM) is a classification and regression method developed by Vapnik.30 In support vector regression (SVR), the input variables are first mapped into a higher dimensional feature space by the use of a kernel function, and then a linear model is constructed in this feature space. The kernel functions often used in SVM include linear, polynomial, radial basis function (RBF), and sigmoid function. The generalization performance of SVM depends on the selection of several internal parameters of the algorithm (C and e), the type of kernel, and the parameters of the kernel.31... 

Li LW, Khanna M, Jo IH et al (2011) Target-specific support vector machine scoring in structure-based virtual screening computational validation, in vitro testing in kinases, and effects on lung cancer cell proliferation. J Chem Inf Model 51(4) 755-759... 

Shen M-Y, Su B-H, Esposito EX et al (2011) A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets. Chem Res Toxicol 24(6) 934-949... 

Key words B-cell epitopes, Linear, Conformational, Support vector machines, Hidden Markov models, Immunoinformatics... 

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