By Enrico Formenti, Roberto Tagliaferri, Ernst Wit (eds.)
This ebook constitutes the completely refereed post-conference
proceedings of the tenth overseas assembly on Computational
Intelligence tools for Bioinformatics and Biostatistics, CIBB 2013, held in great, France in June 2013.
The 19 revised complete papers provided have been rigorously reviewed and
selected from 35 submissions. The papers are prepared in topical
sections on bioinformatics, biostatistics, wisdom established drugs, and knowledge integration and research in omic-science.
Read Online or Download Computational Intelligence Methods for Bioinformatics and Biostatistics: 10th International Meeting, CIBB 2013, Nice, France, June 20-22, 2013, Revised Selected Papers PDF
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Extra info for Computational Intelligence Methods for Bioinformatics and Biostatistics: 10th International Meeting, CIBB 2013, Nice, France, June 20-22, 2013, Revised Selected Papers
Panels on the left show success rates when binding-aﬃnity based (BA) scoring is used and the ones on the right show the same results when ML SFs predicted RMSD values directly. M. R. Mahapatra binding aﬃnity values. In the case of RMSD-based SFs, on the other hand, training dataset size can be increased not only by considering a large number of protein-ligand complexes in the training set, but also by using a larger number of computationally-generated ligand poses per complex since each pose provides a new training record because it corresponds to a diﬀerent combination of features and/or RMSD value.
267(3), 727–748 (1997) 10. : Molecular recognition of the inhibitor AG-1343 by HIV-1 protease: conformationally ﬂexible docking by evolutionary programming. Chem. Biol. 2(5), 317–324 (1995) 11. 0) 12. : DrugScore CSD - knowledge-based scoring function derived from small molecule crystal data with superior recognition rate of near-native ligand poses and better aﬃnity prediction. J. Med. Chem. 48(20), 6296–6303 (2005) 13. : LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites.
For brevity, we only report the version and/or option that yields the best performance on the PDBbind benchmark that was considered by Cheng et al. 5 Machine Learning Methods We utilize a total of six regression techniques in our study: multiple linear regression (MLR), multivariate adaptive regression splines (MARS), k-nearest neighbors (kNN), support vector machines (SVM), random forests (RF), and boosted regression trees (BRT) . These techniques are implemented in the following R language packages that we use : the package stats readily available in R for MLR, earth for MARS , kknn for kNN , e1071 for SVM , randomForest for RF , and gbm for BRT .
Computational Intelligence Methods for Bioinformatics and Biostatistics: 10th International Meeting, CIBB 2013, Nice, France, June 20-22, 2013, Revised Selected Papers by Enrico Formenti, Roberto Tagliaferri, Ernst Wit (eds.)