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 starting to be gradually during the last few years boosted through an expanding desire for computational strategies which could successfully deal with the large quantities of information produced by way of the recent experimental recommendations in Biology. This demands new algorithms and methods from fields resembling information Integration, facts, info Mining, computing device studying, Optimization, machine technology and synthetic Intelligence.
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Extra info for Advances in Bioinformatics: 4th International Workshop on Practical Applications of Computational Biology and Bioinformatics 2010 (IWPACBB 2010)
Proteins can assume catalytic roles and accelerate or inhibit chemical reactions in our body. They can assume roles of transportation of smaller molecules, storage, movement, mechanical support, immunity and control of cell growth and diﬀerentiation . All of these functions rely on the 3D-structure of the protein. The process of going from a linear sequence of amino acids, that together compose a protein, to the protein’s 3D shape is named protein folding. Anﬁnsen’s work  has proven that primary structure determines the way protein folds.
The distribution of samples is showed in Table 1. 28 M. Reboiro-Jato et al. Table 1 Distribution of microarray data samples belonging to the public datasets analyzed Gutiérrez et al Bullinger et al Valk et al APL 10 19 7 Inv(16) 4 14 10 Monocytic 7 64 7 Other 22 177 51 In order to compare the performance obtained by the different ensemble approaches, we have selected four well-known classification algorithms: (i) Naïve Bayes (NB) learner is perhaps the most widely used method. Although its independence assumption is over-simplistic, studies have found NB to be very effective in a wide range of problems; (ii) IB3 represents a variant of the well-known nearest neighbour algorithms implementing a simple version of a lazy learner classifier; (iii) Support Vector Machines (SVMs) constitute a famous family of algorithms used for classification and regression purposes.
All collected sequences where an helix does not start were included in the “nonStartHelix” class. These later sequences include interior of α-helices, end points of α-helices, start, interior and end points of beta strands. 10. We used machine learning 38 R. Camacho et al. 0 toolkit . We used a 10-fold cross validation procedure to estimate the quality of constructed models. We have used rule induction algorithms (Ridor), decision trees (J48  and ADTree ), functional trees (FT ), instance-based learning (IBk ), bayesian algorithms (NaiveBayes and BayesNet ) and an ensemble method (RandomForest ).