Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 10th International M Enrico Formenti,Roberto Tagliaferri,Ernst Wit

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Molecular Docking for Drug Discovery: Machine-Learning Approaches for Native Pose Prediction of Protcking accuracies of these new ML SFs as well as those of conventional SFs in the context of the 2007 PDBbind benchmark datasets on both diverse and homogeneous (protein-family-specific) test sets. We find that the best performing ML SF has a success rate of 80?% in identifying poses that are within

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Reverse Engineering Methodology for Bioinformatics Based on Genetic Programming, Differential Expres procedures for implementing the first step of the four step GP RODES framework together with an application of the GP RODES on a real miRNA dataset. Specifically it is highlighted a robust method to noise for fitting omics experimental input data, which consists of the Smoothing Spline Regression (

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Superresolution MUSIC Based on Mar?enko-Pastur Limit Distribution Reduces Uncertainty and Improves Dandomly selected genes for classifier input. In addition, when BRM was applied to best ranked N genes, the interquartile ranges of accuracy were smaller when compared with direct input of best ranked genes into classifiers. Overall, BRM can optimally be used with 128 or 256 best ranked markers, requ

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https://doi.org/10.1007/978-3-663-08985-8tandard gene identification as keywords. We present BSE architecture and functionality and discuss how our strategies contribute to successfully tackle big data problems in querying gene-based web resources. BSE is publically available at: ..

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