Computational Intelligence Methods for Bioinformatics and Biostatistics - Info and Reading Options

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Title -- Preface -- Special Guest Message for the 150th Anniversary of Italian Unification -- Organization -- Table of Contents -- Invited Lectures -- Modelling the Effect of Genes on the Dynamics of Probabilistic Spiking Neural Networks for Computational Neurogenetic Modelling -- Introduction -- Probabilistic Neural Models -- Modelling the Effect of Gene Dynamics on the Spiking Dynamics of a pSNN for a pCNGM -- Conclusion and Further Research -- References -- Biostatistics Meets Bioinformatics in Integrating Information from Highdimensional Heterogeneous Genomic Data: Two Examples from Rare Genetic Diseases and Infectious Diseases -- Introduction -- Statistics and Bioinformatics in Gene Therapy Frameworks -- Statistics and Bioinformatics in High Incidence Infectious Diseases: An Application to Mycobacterium Tubercolosis -- Methods -- sRNA Candidates Definition -- Candidates sRNA Encoding Region -- Final Comments -- References -- Statistical Learning -- Bayesian Models for the Multi-sample Time-Course Microarray Experiments -- Introduction -- Statistical Modeling, Estimation and Classification of Gene Expression Profiles -- The Data Structure -- Modeling the Gene Expression Profiles -- Modeling the Errors -- Estimation of Gene-Dependent Parameters -- Identification and Classification of Genes -- Evaluation of Class Probabilities -- Identification and Classification of Differentially Expressed Genes -- Estimation of Gene Expression Profiles -- Estimation of Global Parameters and Prior Hyperparameters -- Algorithm -- Simulations Results and Discussion -- References -- A Machine Learning Pipeline for Discriminant Pathways Identification -- Introduction -- Methods -- The Pipeline -- Experimental Setup for the Examples -- Data Description -- Results -- Air Pollution Experiment -- Parkinson Disease Experiment -- Conclusions -- References

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