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Centro de Investigaciones Príncipe Felipe, Bioinformatics and Genomics Department. Valencia, Spain.
Systems Biology discipline focus the study of biology as the compendium of molecular systems that operate in an interconnected and coordinated way. The development of high-throughput omics technologies such as DNA microarrays enabled genome-wide measurements of the activity of cellular elements and provide the first analytical resources for the establishment of the Systems Biology orientation. Thus, analysis and interpretation of microarray gene expression data has evolved from the gene to the pathway and interaction level, i.e. from the detection of differentially expressed genes, to the establishment of gene interaction networks and the identification of enriched functional categories. Still, the understanding of the biological systems requires a higher level of study, which would be characterized by the interaction between functional modules. In this sense, the main goal of our cooperation research activities is focused in the development of a novel methodological approach to data analysis in Systems Biology which enables the definition of networks of relationships between molecular pathways and the study of their changes across experimental situations. Our proposal has been developed for gene expression data but could be easily extended to other types of molecular high-throughput measurements.
School of Computing and Mathematics, University of Ulster. Jordanstown, United Kingdom.
http://www.infj.ulst.ac.uk/cm/
This research line consists in the study of artificial intelligence techniques to infer gene regulatory networks from geneexpression data, and to apply software engineering tools to visualize and model such networks. In particular, we are working in the inference of time delay association rules among genes combining adaptive discretization of microarray data with intelligent classification methods, in order to infer complex functional regulation associations. We are also working in the incorporation of another type of biological data sources to the inference process. As a second phase of this research, we will work in the adaptation of model checking techniques for the verification and evaluation of biological hypothesis about the networks behaviors. Our long-term objective is the development of an integrative bioinformatic tool for the inference, modelling and study of gene regulatory mechanism.
Centro de Química Farmacéutica. Havana, Cuba.
Prediction of physicochemical properties is of major concern for pharmaceutical research. In this context, machine learning methods are of great importance due to their contribution to the development of a plethora of models. In particular, we are working on a novel framework for physicochemical property prediction, where training data is first clustered according to their structural similarity, and a classifier is trained for each conformed cluster. In this regard, the property prediction of a novel candidate drug is modeled by the classifier associated with the cluster that has more structurally-similar compounds with regard to the new putative drug.