Carlos Silva Santos

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Gene regulatory networks inference from gene expression data is an important problem in systems biology field, in which the main goal is to comprehend the global molecular mechanisms underlying diseases for the development of medical treatments and drugs. This problem involves the estimation of the gene dependencies and the regulatory functions governing(More)
In this work we propose the use of independent subspace analysis (ISA) for selecting filters used for texture processing. ISA is an extension of independent component analysis (ICA), a technique employed to decompose an image into statistically independent features. In ISA, complete independence of features is not required; features that possess some mutual(More)
Breast carcinoma is one of the most common neoplasia and the first cause of women cancer related deaths worldwide. In the past few years with diagnostic increment, the number of patients diagnosed with ductal carcinoma in situ (DCIS) increased considerably and opened up new ways in research and new dilemmas in diagnostic and clinical practice. This work(More)
The inference of gene networks from gene expression data is an open problem due to the large dimensionality (number of genes) and the small number of data samples typically available, even considering the fact that the network is sparse (limited number of input genes per target gene). In this work we propose a method that alleviates the curse of(More)
One of the most important problems in the context of systems biology is to infer gene regulatory networks from gene expression data, since most of the control of cellular processes are performed by the multivariate activity of genes by means of their transcribed mRNA expression. Although many methods have been proposed to deal with this problem in the last(More)
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