Gladys Casas Cardoso

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Due to the wide availability of huge amounts of data in electronic forms, the necessity of turning such data into useful knowledge has increased. This is a proposal of learning from examples. In this paper, we propose measures to evaluate the quality of training sets used by algorithms for learning classification. Our training set assessment relies on(More)
A new set of nucleotide-based bio-macromolecular descriptors are presented. This novel approach to bio-macromolecular design from a linear algebra point of view is relevant to nucleic acids quantitative structure-activity relationship (QSAR) studies. These bio-macromolecular indices are based on the calculus of bilinear maps on Re(n)[b(mk)(x (m),y(More)
In the preset report, for the first time, support vector machine (SVM), artificial neural network (ANN), Bayesian networks (BNs), k-nearest neighbor (k-NN) are applied and compared on two "in-house" datasets to describe the tyrosinase inhibitory activity from the molecular structure. The data set Data I is used for the identification of tyrosinase(More)
A successful interpretation of data goes through discovering crucial relationships between variables. Such a task can be accomplished by a Bayesian network. The dark side is that, when lots of variables are involved, the learning of the network slows down and may lead to wrong results. In this study, we demonstrate the feasibility of applying an existing(More)
This paper presents a novel algorithm for ortholog detection that involves the aggregation of similarity measures characterizing the relationship between gene pairs of two genomes. The measures are based on the alignment score, the length of the sequences, the membership in the conserved regions as well as on the protein physicochemical profile. The(More)
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