Bioinformatics and Biomedical Engineering

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Intro -- Preface -- Organization -- Contents - Part I -- Contents -- Part II -- Advances in Computational Intelligence for Critical Care -- Health Monitoring System Based on Parallel-APPROX SVM -- 1 Introduction -- 2 Health Monitoring System in ICU -- 3 Parallel and Approx Support Vector Machines -- 3.1 The Standard SVM -- 3.2 The Parallel SVM: The PSVM -- 3.3 The Approx SVM: The ASVM -- 4 Monitoring System Based on Parallel and Approx SVM -- 5 Experiments -- 5.1 The Framework -- 5.2 Evaluation Criteria -- 5.3 Results and Discussion -- 6 Conclusion -- References -- Machine Learning for Critical Care: An Overview and a Sepsis Case Study -- 1 Introduction -- 2 Machine Learning and Computational Intelligence in Critical Care -- 3 Machine Learning for the Analysis of Sepsis as a Paradigmatic Critical Care Pathology -- 3.1 Sepsis: Some Basic Background -- 3.2 Review of the Definitions of Sepsis Through Causal Probabilistic Networks -- 3.3 Finding Prognostic Factors Through Machine Learning -- 3.4 Sepsis Mortality Prediction from Observed Data -- 4 Conclusions -- References -- Bioinformatics for Healthcare and Diseases -- A Meta-Review of Feature Selection Techniques in the Context of Microarray Data -- Abstract -- 1 Introduction -- 1.1 Microarray Data -- 1.2 The Need for Feature Selection in Microarray Data Analysis -- 1.3 Why a Meta-Review -- 2 Selected Reviews and Taxonomies -- 2.1 Understanding Feature-Selection Taxonomies -- 2.2 Saeys et al., Bioinformatics 2007 [11] -- 2.3 Lazar et al., IEEE 2012 [12] -- 2.4 Bolon-Canedo et al., IS 2014 [13] -- 2.5 Chandrashekar and Sahin, Computers and Electrical Engineering 2014 [14] -- 2.6 Hira and Gillies, Advances in Bioinformatics 2015 [15] -- 3 Proposed Extended Taxonomy -- 4 Conclusion -- References -- Immune Network Technology on the Basis of Random Forest Algorithm for Computer-Aided Drug Design

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