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Titlebook: Computational Methods in Systems Biology; 20th International C Ion Petre,Andrei P?un Conference proceedings 2022 The Editor(s) (if applicab

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發(fā)表于 2025-3-21 19:29:48 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computational Methods in Systems Biology
副標(biāo)題20th International C
編輯Ion Petre,Andrei P?un
視頻videohttp://file.papertrans.cn/233/232797/232797.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Methods in Systems Biology; 20th International C Ion Petre,Andrei P?un Conference proceedings 2022 The Editor(s) (if applicab
描述This book constitutes the refereed proceedings of the 20th International Conference on Computational Methods in Systems Biology, CMSB 2022, held in Bucharest, Romania, in September 2022..The 13 full papers and 4 tool papers were carefully reviewed and selected from 43 submissions. CMSB focuses on modeling, simulation, analysis, design and control of biological systems. The papers are arranged thematically as follows: Chemical reaction networks; Boolean networks; continuous and hybrid models; machine learning; software..
出版日期Conference proceedings 2022
關(guān)鍵詞artificial intelligence; communication systems; computer networks; computer science; computer systems; en
版次1
doihttps://doi.org/10.1007/978-3-031-15034-0
isbn_softcover978-3-031-15033-3
isbn_ebook978-3-031-15034-0Series 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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Prioritization of?Candidate Genes Through Boolean Networks data. Then, we describe a method to identify master regulatory genes, which have an impact on the dynamics of the gene regulation in a specific disease-related transcriptional context. We showed that our novel method for the identification of master regulatory genes was consistent with network cont
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Probabilistic Multivariate Early Warning Signalsction by more efficiently utilizing the available information from multivariate time series. In particular, we consider a probabilistic variant of a vector autoregression model as a novel early warning indicator and argue that it has certain advantages in model regularization, treatment of uncertain
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https://doi.org/10.1007/978-3-540-71593-1ransition graph that is reachable from the abstraction of the initial state. We prove the soundness of our abstract simulation algorithm, and show its applicability to reaction networks in the SBML format from the BioModels database.
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