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

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發(fā)表于 2025-3-21 17:23:20 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Computational Intelligence Methods for Bioinformatics and Biostatistics
副標(biāo)題17th International M
編輯Davide Chicco,Angelo Facchiano,Paolo Cazzaniga
視頻videohttp://file.papertrans.cn/233/232394/232394.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Intelligence Methods for Bioinformatics and Biostatistics; 17th International M Davide Chicco,Angelo Facchiano,Paolo Cazzanig
描述.This book constitutes revised selected papers from the 17th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics, CIBB 2021, which was held virtually during November 15–17, 2021..The 19 papers included in these proceedings were carefully reviewed and selected from 26 submissions, and they focus on bioinformatics, computational biology, health informatics, cheminformatics, biotechnology, biostatistics, and biomedical imaging..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; biostatistics; computational and systems biology; computer networks; computer s
版次1
doihttps://doi.org/10.1007/978-3-031-20837-9
isbn_softcover978-3-031-20836-2
isbn_ebook978-3-031-20837-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 22:11:39 | 只看該作者
Biochemische Individualit?t und Gichtects showing PLM (mainly restless legs syndrome patients). Despite its many simplifying assumptions—the strongest being the stationarity of the neural processes during night sleep—the model simulations are in remarkable agreement with the polysomnographically recorded data.
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發(fā)表于 2025-3-22 00:44:39 | 只看該作者
https://doi.org/10.1007/978-3-642-94920-3 addition, automatic averaging and aligning of 2D-CNN gradient-based images is applied and shown to improve its biological meaning. The proposed model predicts soft biological brain ageing indicators with a six-class-balanced accuracy of . by using the anagraphic age of 1100 healthy subjects in comparison to their brain scans.
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發(fā)表于 2025-3-22 07:15:08 | 只看該作者
Real-Time Automatic Plankton Detection, Tracking and Classification on Raw Hologram,ideos of raw holograms. Experiments show that our pipeline based on YOLOv5 and SORT is fast (44 FPS) and can accurately detect and identify the plankton among 13 classes (97.6% mAP@0.5, 92% MOTA). Our method can be implemented to detect and count other microscopic objects in raw holograms.
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發(fā)表于 2025-3-22 10:52:17 | 只看該作者
The First , Model of Leg Movement Activity During Sleep,ects showing PLM (mainly restless legs syndrome patients). Despite its many simplifying assumptions—the strongest being the stationarity of the neural processes during night sleep—the model simulations are in remarkable agreement with the polysomnographically recorded data.
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發(fā)表于 2025-3-22 15:23:35 | 只看該作者
,Soft Brain Ageing Indicators Based on?Light-Weight LeNet-Like Neural Networks and?Localized 2D Brai addition, automatic averaging and aligning of 2D-CNN gradient-based images is applied and shown to improve its biological meaning. The proposed model predicts soft biological brain ageing indicators with a six-class-balanced accuracy of . by using the anagraphic age of 1100 healthy subjects in comparison to their brain scans.
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,Summarizing Global SARS-CoV-2 Geographical Spread by?Phylogenetic Multitype Branching Models,ormation on the place of sampling of each strain. We find that even with such coarse–grained data the dominating transition rates exhibit weak similarities with the most popular, continent–level aggregated, airline passenger flight routes.
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發(fā)表于 2025-3-23 05:51:19 | 只看該作者
0302-9743 d and selected from 26 submissions, and they focus on bioinformatics, computational biology, health informatics, cheminformatics, biotechnology, biostatistics, and biomedical imaging..978-3-031-20836-2978-3-031-20837-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
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