Carlos Eduardo Ferreira
University of São Paulo
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Publication
Featured researches published by Carlos Eduardo Ferreira.
BMC Bioinformatics | 2006
André Fujita; João Ricardo Sato; Leonardo de Oliveira Rodrigues; Carlos Eduardo Ferreira; Mari Cleide Sogayar
BackgroundWith the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration.ResultsHere, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets.ConclusionIn face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.
BMC Systems Biology | 2007
André Fujita; João Ricardo Sato; Humberto Miguel Garay-Malpartida; Rui Yamaguchi; Satoru Miyano; Mari Cleide Sogayar; Carlos Eduardo Ferreira
BackgroundTo understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems.ResultsWe have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets.ConclusionThe proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any a priori information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.
Siam Journal on Optimization | 1996
Carlos Eduardo Ferreira; Alexander Martin; Robert Weismantel
In this paper we consider the multiple knapsack problem, which is defined as follows: given a set
Bioinformatics | 2007
André Fujita; João Ricardo Sato; Humberto Miguel Garay-Malpartida; Pedro A. Morettin; Mari Cleide Sogayar; Carlos Eduardo Ferreira
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Siam Journal on Optimization | 1998
Ralf Borndörfer; Carlos Eduardo Ferreira; Alexander Martin
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Journal of Bioinformatics and Computational Biology | 2008
André Fujita; João Ricardo Sato; Humberto Miguel Garay-Malpartida; Mari Cleide Sogayar; Carlos Eduardo Ferreira; Satoru Miyano
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Journal of Bioinformatics and Computational Biology | 2009
André Fujita; João Ricardo Sato; Marcos Angelo Almeida Demasi; Mari Cleide Sogayar; Carlos Eduardo Ferreira; Satoru Miyano
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Information Processing Letters | 2007
Paulo Feofiloff; Cristina G. Fernandes; Carlos Eduardo Ferreira; José Coelho de Pina
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Journal of Mathematical Imaging and Vision | 2000
Ronaldo Fumio Hashimoto; Junior Barrera; Carlos Eduardo Ferreira
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latin american algorithms graphs and optimization symposium | 2010
Said Sadique Adi; Marília D. V. Braga; Cristina G. Fernandes; Carlos Eduardo Ferreira; Fábio Viduani Martinez; Marie-France Sagot; Marco A. Stefanes; Christian Tjandraatmadja; Yoshiko Wakabayashi
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