Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 17th International M Davide Chicco,Angelo Facchiano,Paolo Cazzanig

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Computational Intelligence Methods for Bioinformatics and Biostatistics978-3-031-20837-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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https://doi.org/10.1007/978-3-031-20837-9artificial intelligence; biostatistics; computational and systems biology; computer networks; computer s

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Biochemische Individualit?t und Gicht can be used for modeling organisms’ features and behaviors. The recent Synthetic Biology advancements in the so-called “synthetic cells” area allow the construction of cell-like systems with non trivial complexity, paving the way to a novel direction: the realization of chemical artificial intellig

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Biochemische Individualit?t und Gicht prediction, such as using machine learning and other statistical methods. However, many of these methods cannot properly capture complex relationships between variables that affect results of odds ratios unless independence between risk factors is assumed. This work addresses this limitation using

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E?st?rungen: überblick aus klinischer Sichts been used to detect and count various microscopic objects and has been applied in submersible equipment to monitor the . distribution of plankton. To count and classify plankton, conventional methods require a holographic reconstruction step to decode the hologram before identifying the objects. H

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E?st?rungen: überblick aus klinischer Sicht The large diversity of sRNAs in terms of their length, sequence, and function poses a challenge for computational sRNA prediction. There are several bacterial sRNA prediction tools. Most of them use sequence-derived features or rely on phylogenetic conservation. Recently, a new sRNA predictor (sRNA