By Joao Carlos Setubal, Sergio Verjovski-Almeida
This e-book constitutes the refereed court cases of the Brazilian Symposium on Bioinformatics, BSB 2005, held in Sao Leopoldo, Brazil in July 2005.
The 15 revised complete papers and 10 revised prolonged abstracts provided including three invited papers have been conscientiously reviewed and chosen from fifty five submissions. The papers tackle a vast variety of present subject matters in computational biology and bioinformatics.
Read or Download Advances in Bioinformatics and Computational Biology: Brazilian Symposium on Bioinformatics, BSB 2005, Sao Leopoldo, Brazil, July 27-29, 2005, Proceedings PDF
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Additional info for Advances in Bioinformatics and Computational Biology: Brazilian Symposium on Bioinformatics, BSB 2005, Sao Leopoldo, Brazil, July 27-29, 2005, Proceedings
Keywords. protein cellular localization, Machine Learning, multiclass Support Vector Machines, Decision Trees. 1 Introduction Proteins may be located at various regions in the cell or transported to the extracellular space . The identiﬁcation of a protein destination is important to understand its function. Even knowing the protein function, its localization may provide valuable information about enzyme pathways . Several works employed Machine Learning (ML) techniques in this recognition task [6, 9, 10, 11, 14].
Using this weighting, classes more distant according to their sample distribution are grouped by the MST algorithm. 5. Scatter Measure: using concepts from , the weight of an arc (i, j) in this method is given by a scattering measure between classes i and j. This measure is calculated by Equation 3, where s2i and s2j are the variances of data samples from classes i and j, respectively. The MST will group classes considered less separated according to the scattering measure calculated. sm (i, j) = µi − µj s2i + s2j 2 (3) 1 6.
Information collected from the training dataset is used to obtain the weighted graph, which has k vertices and k(k − 1)/2 edges connecting all pairs of vertices. Figure 2a illustrates an example of a graph for a problem with ﬁve classes, while Figure 2b shows the MST extracted from this graph. Various methods can be used to assign costs to the edges. In this work, the following approaches are investigated: 1. Centroid distances: each class is ﬁrst represented by a centroid µi . The weight of an arc (i, j) is then given by the Euclidean distance between µi and µj .