Titlebook: Computational Advances in Bio and Medical Sciences; 11th International C Mukul S. Bansal,Ion M?ndoiu,Alexander Zelikovsky Conference procee

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書目名稱Computational Advances in Bio and Medical Sciences影響因子(影響力)




書目名稱Computational Advances in Bio and Medical Sciences影響因子(影響力)學科排名




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書目名稱Computational Advances in Bio and Medical Sciences網(wǎng)絡公開度學科排名




書目名稱Computational Advances in Bio and Medical Sciences被引頻次




書目名稱Computational Advances in Bio and Medical Sciences被引頻次學科排名




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書目名稱Computational Advances in Bio and Medical Sciences年度引用學科排名




書目名稱Computational Advances in Bio and Medical Sciences讀者反饋




書目名稱Computational Advances in Bio and Medical Sciences讀者反饋學科排名

ALLEY

Peter Schuster,Peter F. Stadleriven protein sequence with high accuracy. In contrast, work on deep learning frameworks that can account for the structural plasticity of protein molecules remains in its infancy. Many researchers are now investigating deep generative models to explore the structure space of a protein. Current model

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Nikolay V. Dokholyan,Eugene I. Shakhnovich fishery farming environments are vulnerable to bacteria or viruses and would cause serious losses. Predicting epitope binding segments from pathogenic bacteria is the first step for vaccine and drug development, and bioinformatics technologies could provide effective approaches to facilitate effect

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https://doi.org/10.1007/978-1-0716-0270-6of global deaths every year. However, the images acquired in these procedures have low resolution and poor contrast, making lesion detection and assessment challenging. Accurate coronary artery segmentation not only helps mitigate these problems, but also allows the extraction of relevant anatomical

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Dániel Dudola,Bertalan Kovács,Zoltán Gáspári We detail the CNF formulations for two: the Minimum-Reticulation Problem, and the Hybridization-Network Problem; and apply them to widely studied Grass data, and to randomly generated data. The results reduce the number of reticulations in 4 of the 18 Grass datasets, and improve the best SAT-based

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Biomacromolecular Fragments and Patternsegression, clustering and dimensionality reduction techniques have been widely used in clinical studies to assist health professionals in screening, risk estimation, diagnostics and prognostics. Prospective studies often involve a long follow-up period and a large sample, therefore many investigatio

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