By Joao Carlos Setubal, Sergio Verjovski-Almeida
This ebook 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 offered including three invited papers have been rigorously reviewed and chosen from fifty five submissions. The papers tackle a vast variety of present themes 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 resources for Advances in Bioinformatics and Computational Biology: Brazilian Symposium on Bioinformatics, BSB 2005, Sao Leopoldo, Brazil, July 27-29, 2005, Proceedings
Based on the decision from the previous levels, new nodes are visited, until a leaf node is reached, where the ﬁnal classiﬁcation is given. C. F. de Carvalho Fig. 2. (a) Graph for a problem with five classes; (b) Minimum Spanning Tree; (c) Multiclass hierarchical structure obtained be consulted for each prediction. In the hierarchical structure used, a lower number of binary classiﬁers is also induced (k − 1), so the time spent on the SVM classiﬁers training can be reduced too. This type of hierarchical architecture is also used in [4, 20, 23].
The FBA analysis allows an inﬁnite number of ﬂuxes, many of these ﬂux vectors violate the second law and hence are unfeasible. From a network topology point of view, the presence of cycles in the ﬂux direction violates the law of production of entropy. Applying Kirchoﬀ’s loop law, eliminates these entropy violating cycles. From S we remove the columns corresponding to boundary ﬂuxes and keep only the columns of non-redundant internal ﬂuxes, which we deﬁne as those between metabolites. The resulting matrix G ∈ Rm×ni , where ni is the number of internal ﬂuxes in the network.
Since the analysis and techniques presented here are simple, they can easily be applied to large-scale metabolic networks. Acknowledgement Part of this work was done while the ﬁrst author was visiting the International Computer Science Institute at Berkeley. He would like to thank Prof. Richard Karp for helpful discussions. We also thank Prof. Robert Laughlin of the department of Physics at Stanford for his support of this project. This work was supported by a joint NASA-NCI biotechnology research program.