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Titlebook: Computational Advances in Bio and Medical Sciences; 10th International C Sumit Kumar Jha,Ion M?ndoiu,Alex Zelikovsky Conference proceedings

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書目名稱Computational Advances in Bio and Medical Sciences
副標(biāo)題10th International C
編輯Sumit Kumar Jha,Ion M?ndoiu,Alex Zelikovsky
視頻videohttp://file.papertrans.cn/233/232077/232077.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Advances in Bio and Medical Sciences; 10th International C Sumit Kumar Jha,Ion M?ndoiu,Alex Zelikovsky Conference proceedings
描述This book constitutes the proceedings of the 10th International Conference on Computational Advances in Bio and Medical Sciences, ICCABS 2020, held in December 2020. Due to COVID-19 pandemic the conference was held virtually..The 6 regular and 5 invited papers presented in this book were carefully reviewed and selected from 16 submissions. The use of high throughput technologies is fundamentally changing the life sciences and leading to the collection of large amounts of biological and medical data. The papers show how the use of this data can help expand our knowledge of fundamental biological processes and improve human health - using novel computational models and advanced analysis algorithms..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; clustering algorithms; clustering methods; computer networks; computer science;
版次1
doihttps://doi.org/10.1007/978-3-030-79290-9
isbn_softcover978-3-030-79289-3
isbn_ebook978-3-030-79290-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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Computational Study of Action Potential Generation in Urethral Smooth Muscle Cellto stress urinary incontinence by generating spontaneous mechanical and electrical activities. It generates spontaneous electrical events in the terms of membrane depolarization and action potentials. Therefore, a complete understanding of the urethral smooth muscle cell’s spontaneous action potenti
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Metabolic Pathway Prediction using Non-negative Matrix Factorization with?Improved Precisiony and completion. However, several challenges including pathway features engineering, multiple mapping of enzymatic reactions and emergent or distributed metabolism within populations or communities of cells can limit prediction performance. In this paper, we present triUMPF, .ple non-negative matri
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A Novel Pathway Network Analytics Method Based on Graph Theoryexample, by controlling metabolite synthesis or by regulating the expression of sets of genes. They play a key role in advanced studies of genomics. However, existing pathway analytics methods are inadequate to extract meaningful biological structure underneath the network of pathways. They also lac
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: Ancestral Flowering Plant Chromosomes and Gene Orders Based on Generalized Adjacencies and Chromos difficult by the cycles of whole genome doubling followed by fractionation in plant lineages. Fractionation scrambles the gene adjacencies that enable existing reconstruction methods. We propose an alternative approach that postpones the selection of gene adjacencies for reconstructing small ancest
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