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

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Intro -- Preface -- Organization -- Contents -- Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI -- 1 Can ``Synthetic Cell'' Biotechnology Become a Useful Platform for Chemical AI? -- 2 Scientific Background - What Exactly are SCs? -- 2.1 Computer Gestalt ch1varelabook vs. Autopoiesis &amp -- Autonomy -- 3 Bio-Chemical Neural Network -- 3.1 Selected Examples of Potentially Interesting CNNs for SCs -- 4 Concepts and Experimental Perspectives on Chemical Neural Networks and Synthetic Cells -- 4.1 Machine Learning -- 4.2 Meaning -- 4.3 Embodiment -- References -- Development of Bayesian Network for Multiple Sclerosis Risk Factor Interaction Analysis -- 1 Introduction -- 2 Previous Work -- 2.1 Artificial Intelligence (AI) and Machine Learning (ML) in MS Research -- 2.2 Alignment with Epidemiology -- 3 BN Development -- 3.1 Relevant Risk Factors -- 3.2 Structure -- 3.3 Measurements -- 4 Results and Discussion -- 4.1 Interaction, Sufficiency, Necessity -- 4.2 Equivalent Odds Ratios -- 5 Conclusions -- References -- Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram -- 1 Introduction -- 2 Materials and Methods -- 2.1 Hologram Formation -- 2.2 Dataset -- 2.3 Object Detection Models and Tracking -- 2.4 Metrics -- 3 Results -- 3.1 Detection Performances -- 3.2 Tracking Performances -- 4 Conclusion and Perspectives -- References -- The First in-silico Model of Leg Movement Activity During Sleep -- 1 Scientific Background -- 2 Materials and Methods -- 2.1 The LMA Model -- 2.2 Model Calibration -- 3 Results and Discussion -- 4 Conclusion -- References -- Transfer Learning and Magnetic Resonance Imaging Techniques for the Deep Neural Network-Based Diagnosis of Early Cognitive Decline and Dementia -- 1 Introduction -- 2 Deep Learning for Medical Diagnosis

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