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 progressively over the past few years boosted through an expanding desire for computational strategies which can successfully deal with the massive quantities of knowledge produced by way of the hot experimental ideas in Biology. This demands new algorithms and methods from fields comparable to information Integration, information, facts 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)
We may, therefore, succeed in identifying genetic variants that might otherwise have remained undetected . 2 Methods In this section we present a brief description of the methods that were used and describe our experimental procedure. 1 Data Description We used a dataset produced by the Department of Genetics of the Faculty of Medical Sciences of Universidade Nova de Lisboa with 164 controls and 94 cases, all of them being portuguese caucasians.
We have deﬁned two class values: a class of sequences were an α-helix starts forming and; all other types of sequences where an α-helix does not start. We have applied a set of Machine Learning algorithms and almost all of them made predictions above the naive procedure of predicting the majority class. 4% accuracy with an ensemble algorithm called Random Forest. We have also managed to construct a small decision tree that has smaller accuracy than 80%, but that is an intelligible model that can help in unveiling the chemical justiﬁcation of the formation of α-helices.
Seventy-ﬁve percent accuracy in protein secondary structure prediction. Proteins 27, 329–335 (1997) 8. : Protein secondary structure prediction based on position-speciﬁc scoring matrices. Journal of Molecular Biology 292, 195–202 (1999) 9. : Improvements in protein secondary structure prediction by an enhanced neural network. Journal of Molecular Biology 216, 441–457 (1990) 10. : A study on amino acid pairing at the nand c-termini of helical segments in proteins. PROTEINS: Structure, Function, and Bioinformatics 70(1), 188–196 (2008) 11.
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.)