News Column

New Information Technology Findings from F.M. Lopes and Co-Authors Described

June 24, 2014



By a News Reporter-Staff News Editor at Information Technology Newsweekly -- Investigators publish new report on Information Technology. According to news originating from Campinas, Brazil, by VerticalNews correspondents, research stated, "An important problem in bioinformatics is the inference of gene regulatory networks (GRNs) from expression profiles. In general, the main limitations faced by GRN inference methods are the small number of samples with huge dimensionalities and the noisy nature of the expression measurements."

Our news journalists obtained a quote from the research, "Alternatives are thus needed to obtain better accuracy for the GRNs inference problem. Many pattern recognition techniques rely on prior knowledge about the problem in addition to the training data to gain statistical estimation power. This work addresses the GRN inference problem by modeling prior knowledge about the network topology. The main contribution of this paper is a novel methodology that aggregates scale-free properties to a classical low-cost feature selection method, known as Sequential Floating Forward Selection (SFFS), for guiding the inference task. Such methodology explores the search space iteratively by applying a scale-free property to reduce the search space. In this way, the search space traversed by the method integrates the exploration of all combinations of predictors set when the number of combinations is small (dimensionality (k) = 3). This process is guided by scale-free prior information. Experimental results using synthetic and real data show that this technique provides smaller estimation errors than those obtained without guiding the SFFS application by the scale-free model, thus maintaining the robustness of the SFFS method."

According to the news editors, the research concluded: "Therefore, we show that the proposed framework may be applied in combination with other existing GRN inference methods to improve the prediction accuracy of networks with scale-free properties."

For more information on this research see: A feature selection technique for inference of graphs from their known topological properties: Revealing scale-free gene regulatory networks. Information Sciences, 2014;272():1-15. Information Sciences can be contacted at: Elsevier Science Inc, 360 Park Ave South, New York, NY 10010-1710, USA. (Elsevier - www.elsevier.com; Information Sciences - www.elsevier.com/wps/product/cws_home/505730)

The news correspondents report that additional information may be obtained from F.M. Lopes, Brazilian Bioethanol Sci & Technol Lab CTBE, Campinas, SP, Brazil. Additional authors for this research include D.C. Martins, J. Barrera and R.M. Cesar.

Keywords for this news article include: Brazil, Campinas, South America, Information Technology

Our reports deliver fact-based news of research and discoveries from around the world. Copyright 2014, NewsRx LLC


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Source: Information Technology Newsweekly


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