Support vector machines based

Rashid, M., Sana, S., Raghava, G.R Support vector machine-based method for predicting sub-cellular localization of mycobacterial proteins using evolutionary information and motifs. BMC Bioinformatics 2007, 8,337. 

Garg, A., Bhasin, M., Raghava, G.RS. Support vector machine-based method for subceUular localization of human proteins using amino acid compositions, then order, and similarity search. 

Wang, L.-H., Liu, J., Li, Y.-R, Zhou, H.-B. Predicting protein secondary structure by a support vector machine based on a new coding scheme. Genome Inform. 2004,15,181-90. 

Geppert, H., Horvath, T., Gartner, T., Wrobel, S., and Bajorath, J. (2008) Support-vector-machine-based ranking significantly improves the effectiveness of similarity searching using... 

Uncovering Predictive Genes using Support Vector Machines based on Model Population Analysis, IEEE/ ACM T Comput Bi, 8 (2011) 1633. 

C. S. Nandi, B. Tudu, C. Koley, Support vector Machine based maturity prediction, in WASET, International Conference ICCESSE-2012, 2012, pp. 1811-1815... 

K. Suykens, A tutorial on support vector machine-based methods for classification problems in chemometrics. Anal. Chim. Acta, 2010, 665, 129-145. 

Cristianini N, Shawe-Taylor J. An introduction to support vector machines and other kernel-based learning methods. Cambridge, UK Cambridge University Press, 2000. 

Christianini, N., Shawe-Taylor, J. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, NY, 2000. Crawford, L. R., Morrison, J. D. Anal. Chem. 40, 1968, 1469-1474. Computer methods in analytical mass spectrometry. Empirical identification of molecular class. 

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. 

Jia, L. and Sun, H. (2008) Support vector machines classification of hERG liabilities based on atom types. Bioorganic ei Medicinal Chemistry, 16, 6252—6260. 

Support vector machines In addition to more traditional classification methods like clustering or partitioning, other computational approaches have recently also become popular in chemoinformatics and support vector machines (SVMs) (Warmuth el al. 2003) are discussed here as an example. Typically, SVMs are applied as classifiers for binary property predictions, for example, to distinguish active from inactive compounds. Initially, a set of descriptors is selected and training set molecules are represented as vectors based on their calculated descriptor values. Then linear combinations of training set vectors are calculated to construct a hyperplane in descriptor space that best separates active and inactive compounds, as illustrated in Figure 1.9. 

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... 

More complex approaches to this problem involve the use of artificial neural networks [22], Bayesian networks [23] and support vector machines [24], which in turn are based on the same principle of supervised learning [25]. 

Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, Furey TS Jr, Ares M, Haussler D, Knowledge-based analysis of microarray gene expression data by using support vector machines, Proc. Natl. Acad. Sci. USA, 97 262-267, 2000. 

Cristianini, N. and Shawe-Taylor, J., An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, UK, 2000. 

IUPAC-like expressions, true IUPAC nomenclature names, and InChl and SMILES representations of chemical compounds are well suited for detection by machine learning approaches. Conditional random fields (CRFs)41 and support vector machines have been used for the detection of IUPAC expressions in scientific literature 42 Other approaches are based on rules sets43 44 or combinations of machine learning with rule-based approaches 45 All these approaches have in common that they face one significant problem the name-to-structure problem. 

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