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

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Intro -- Preface -- Organization -- Contents -- Keynote Speaker -- Dynamic Gaussian Graphical Models for Modelling Genomic Networks -- 1 Introduction -- 2 Graphical Models -- 3 Dynamic Gaussian Graphical Model for Networks -- 3.1 Sparsity Restrictions of the Precision Matrix -- 3.2 Model Restrictions of the Precision Matrix -- 3.3 Maximum Likelihood -- 4 Max Determinant Optimization Problem -- 5 Application to T-Cell Data -- 6 Conclusions -- References -- Bioinformatics Regular Session -- Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protein-Ligand Complexes -- 1 Introduction -- 1.1 Background -- 1.2 Related Work -- 1.3 Key Contributions -- 2 Materials and Methods -- 2.1 Compound Database -- 2.2 Compound Characterization -- 2.3 Decoy Generation and Formation of Training and Test Sets -- 2.4 Conventional Scoring Functions -- 2.5 Machine Learning Methods -- 3 Results and Discussion -- 3.1 Evaluation of Scoring Functions -- 3.2 ML vs. Conventional Approaches on a Diverse Test Set -- 3.3 ML vs. Conventional Approaches on Homogeneous Test Sets -- 3.4 Impact of Training Set Size -- 4 Conclusion -- References -- BioCloud Search EnGene: Surfing Biological Data on the Cloud -- Abstract -- 1 Introduction -- 2 Background and Motivations -- 3 Architectural Aspects -- 3.1 Query Contextualization -- 3.2 Technical Details -- 4 BSE Functionalities -- 5 Conclusions -- Acknowledgments -- References -- Genomic Sequence Classification Using Probabilistic Topic Modeling -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Probabilistic Topic Models -- 3.2 Document Model and DNA Sequences -- 4 Experimental Tests -- 4.1 Bacteria Dataset -- 4.2 Training and Testing Pipelines -- 4.3 Classification Pipeline -- 4.4 Results and Discussion -- 5 Conclusion and Future Work -- References

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