By K. S. Machado, E. K. Schroeder, D. D. Ruiz, O. Norberto de Souza (auth.), Marie-France Sagot, Maria Emilia M. T. Walter (eds.)
Read Online or Download Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings PDF
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Extra info for Advances in Bioinformatics and Computational Biology: Second Brazilian Symposium on Bioinformatics, BSB 2007, Angra dos Reis, Brazil, August 29-31, 2007. Proceedings
The approach proposed is based on our Multi-Objective Clustering Ensemble algorithm (MOCLE). This algorithm generates a concise and stable set of partitions, which represents diﬀerent trade-oﬀs between several measures of partition quality. The prior knowledge is automatically integrated in MOCLE by embedding it into one of the objective functions. In this case, the function gives as output the quality of a partition, considering the prior knowledge of one of the known structures of the data. 1 Introduction Cluster analysis has been largely employed to address diﬀerent kinds of problems in Bioinformatics, ranging from the identiﬁcation of genes function to the discovery of groups and subgroups of diseases [1,2].
However, the position based algorithm takes less time in all instances. The reason of these results is that the objective function is computationally more expensive in the MbGA than in the PbGA. Another set of experiments were included allowing the PbGA to have the same or more computation time than the MbGA by increasing the population size from 1000 to 3000 individuals in the PbGA. The results are shown in Table 3. We can see here that no signiﬁcant improvement is achieved by the PbGA. In the same table results given by the Gibbs sampler  are also shown.
9] investigated the downregulation activity of the carnitine palmitoyltransferase (CPT) system in the development of novel drugs for diabetes treatment. CPT1A and CPT2 genes are involved in the fatty acid metabolism and are members of this pathway. Additionally, Wood  states that the presence of a large amount of fatty acids and a low fatty acid oxidation may cause insulin resistance and, eventually, type 2 diabetes mellitus. The use of the GSEA method in combination with the BW S metric resulted in the detection of three extra signiﬁcant gene sets.