By Liliana López Kleine, Víctor Andrés Vera Ruiz (auth.), Miguel P. Rocha, Florentino Fernández Riverola, Hagit Shatkay, Juan Manuel Corchado (eds.)
The fields of Bioinformatics and Computational Biology were turning out to be progressively over the past few years boosted through an expanding desire for computational concepts which may successfully deal with the massive quantities of information produced by way of the hot experimental concepts in Biology. This demands new algorithms and methods from fields similar to information Integration, information, information Mining, desktop studying, Optimization, computing device technological know-how and synthetic Intelligence.
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Extra resources for Advances in Bioinformatics: 4th International Workshop on Practical Applications of Computational Biology and Bioinformatics 2010 (IWPACBB 2010)
Peng  presented a novel ensemble approach based on seeking an optimal and robust combination of multiple classifiers. The proposed algorithm begins with the generation of a pool of candidate base classifiers based on the gene subsampling and then, it performs the selection of a sub-set of appropriate base classifiers to construct the classification committee based on classifier clustering. Experimental results demonstrated that the proposed approach outperforms both baseline classifiers and those generated by bagging and boosting.
We ﬁrst downloaded a list of low homology proteins from the Dunbrak web site 1 . The downloaded list contained 1499 low homology proteins. We then downloaded the PDBs2 for each of the protein in the list. Each PDB was then processed in order to extract secondary structure information and the linear sequence of residues of the protein. Each example of a data set is a sequence of a ﬁxed number of residues (window) before and after the start or end of a secondary structure. The window size is ﬁxed for each data set and we have produced 4 data sets using 4 diﬀerent window sizes.
In addition, the four simple base classifiers used for the ensemble generation (IB3, NBS, RF, SVM) where also tested individually, using as feature selection both those genes included in the OMIM gene sets plus those genes being part of the KEGG gene sets. Classification tests were performed using a stratified 10-fold crossvalidation. Tables 2 and 3 summarize the results obtained from the experimentation carried out showing only those classifiers with better performance. Table 2 presents the accuracy and kappa values achieved by each classifier using KEGG gene sets as prior knowledge.
Advances in Bioinformatics: 4th International Workshop on Practical Applications of Computational Biology and Bioinformatics 2010 (IWPACBB 2010) by Liliana López Kleine, Víctor Andrés Vera Ruiz (auth.), Miguel P. Rocha, Florentino Fernández Riverola, Hagit Shatkay, Juan Manuel Corchado (eds.)