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By Liliana López Kleine, Víctor Andrés Vera Ruiz (auth.), Miguel P. Rocha, Florentino Fernández Riverola, Hagit Shatkay, Juan Manuel Corchado (eds.)

ISBN-10: 3642132138

ISBN-13: 9783642132131

ISBN-10: 3642132146

ISBN-13: 9783642132148

The fields of Bioinformatics and Computational Biology were becoming gradually over the past few years boosted through an expanding desire for computational ideas which could successfully deal with the massive quantities of knowledge produced by means of the recent experimental suggestions in Biology. This demands new algorithms and ways from fields reminiscent of info Integration, facts, info Mining, desktop studying, Optimization, computing device technological know-how and synthetic Intelligence.

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Additional info for Advances in Bioinformatics: 4th International Workshop on Practical Applications of Computational Biology and Bioinformatics 2010 (IWPACBB 2010)

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We have selected samples from each dataset belonging to 4 different groups of acute myeloid leukemias including (i) promyelocytic (APL), (ii) inversion 16, (iii) monocytic and (iv) other AMLs. 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.

30 M. Reboiro-Jato et al. Table 2 Classification result using KEGG gene sets Classifier Gutiérrez Bullinger Valk Accuracy Kappa Accuracy Kappa Accuracy AC 76,74% 0,588 76,00% 0,292 76,28% Kappa 0,503 AG 79,07% 0,634 76,00% 0,299 75,18% 0,533 BCbyT_90% 81,40% 0,724 82,67% 0,373 77,01% 0,540 BCbyT_95% 83,72% 0,724 82,67% 0,329 78,83% 0,574 BCw/oT_60% 79,07% 0,635 80,00% 0,476 77,37% 0,556 BCw/oT_75% 79,07% 0,635 81,33% 0,367 76,64% 0,555 BCw/oT_85% 76,74% 0,612 80,00% 0,403 75,55% 0,546 IB3 83,72% 0,756 69,33% 0,369 67,52% 0,410 NBS 81,40% 0,679 73,33% 0,269 74,09% 0,530 RF 72,09% 0,533 68,00% 0,123 69,71% 0,337 SVM 51,16% 0,000 68,00% 0,000 64,60% 0,000 Table 3 Classification result using OMIM gene sets Classifier AC AG BCbyT_90% BCbyT_95% BCw/oT_60% BCw/oT_75% BCw/oT_85% IB3 NBS RF SVM Gutiérrez Accuracy Kappa 76,74% 0,588 76,74% 0,588 81,40% 0,680 86,05% 0,774 81,40% 0,680 88,37% 0,809 79,07% 0,672 76,74% 0,643 79,07% 0,634 79,07% 0,658 51,16% 0,000 Bullinger Accuracy Kappa 76,00% 0,343 76,00% 0,343 82,67% 0,569 82,67% 0,569 80,00% 0,483 81,33% 0,526 80,00% 0,482 73,33% 0,451 76,00% 0,370 74,67% 0,372 68,00% 0,000 Valk Accuracy Kappa 74,82% 0,439 76,28% 0,506 75,91% 0,528 75,91% 0,513 75,91% 0,521 75,91% 0,530 76,28% 0,555 67,52% 0,391 72,99% 0,510 74,09% 0,420 77,74% 0,539 Table 3 presents the same experimentation but using the OMIM gene sets.

A case-control study was performed, composed of 164 controls and 94 cases with 32 SNPs available from the BRCA1, BRCA2 and TP53 genes. There was also information about tobacco and alcohol consumption. We used a Decision Tree to find a group with high-susceptibility of suffering from breast cancer. Our goal was to find one or more leaves with a high percentage of cases and small percentage of controls. To statistically validate the association found, permutation tests were used. pt Bruno C. pt Arlindo L.

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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.)


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