Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 7th International Me Riccardo Rizzo,Paulo J. G. Lisboa Conference

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MOSCFRA: A Multi-objective Genetic Approach for Simultaneous Clustering and Gene Ranking genes. Clustering the tissue samples according to their co-expressed behavior and characteristics is an important tool for partitioning the dataset. Finding the clusters of a given dataset is a difficult task. This task of clustering is even more difficult when we try to find the rank of each gene,

斗志

A Multi-relational Learning Framework to Support Biomedical Applicationse popularity reached in the field of bioinformatics. In particular we focus our attention on the domain of assisted reproduction techniques with particular interest on the field of intracytoplasmic sperm injection. In this paper we would provide a multi-relational learning framework able to discover

惰性女人

Chauvinistic

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Dynamic Simulations of Pathways Downstream of ERBB-Family: Exploration of Parameter Space and Effectess of function of dominant onco-proteins with onco-protein inhibitors. We studied and implemented dynamic simulations of four downstream pathways. The fragment of the signaling-network we evaluated was described as a Molecular Interaction Map. Our simulations involved 242 modified species and compl

DNR215

Robustness Analysis of a Linear Dynamical Model of the , Gene Expressionof a microarray time series involving the expression levels of more than 4000 genes over 67 time-points, and has been modeled by a system of linear differential equations with constant coefficients. Here we investigate the robustness of this model against perturbations of its parameters and of the i

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Qualitative Reasoning on Systematic Gene Perturbation Experimentsd noisy simulated data. This, together with the polynomial running time, makes our algorithm an useful tool for systematic gene perturbation experiments, able to identify a subset of the oriented regulatory relations with high reliability and to provide valuable insights on the amount of information conveyed by a set of experiments.

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Robustness Analysis of a Linear Dynamical Model of the , Gene Expressionnitial data values. We found that the model is not robust at all for fully connected networks, but that the robustness significantly increases after parameter reduction. This puts some limits to the biological relevance of linear models for gene expression evolution.