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Titlebook: Information Technology in Bio- and Medical Informatics; 6th International Co M. Elena Renda,Miroslav Bursa,Sami Khuri Conference proceeding

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樓主: 弄碎
31#
發(fā)表于 2025-3-26 22:22:49 | 只看該作者
32#
發(fā)表于 2025-3-27 01:50:09 | 只看該作者
Pinaki Bhaskar,Marina Buzzi,Filippo Geraci,Marco Pellegrini continuity management courses, this book also addresses researchers and professionals in the broad field of IT Security and cyber resilience..978-3-031-52640-4978-3-031-52064-8Series ISSN 2198-4182 Series E-ISSN 2198-4190
33#
發(fā)表于 2025-3-27 07:04:34 | 只看該作者
From Literature to Knowledge: Exploiting PubMed to Answer Biomedical Questions in Natural Languaged as a subgraph matching problem onto the internal graph representation, and thus can handle efficiently also partial matches. Preliminary user-based output quality measurements confirm the viability of our method.
34#
發(fā)表于 2025-3-27 10:22:32 | 只看該作者
A Logistic Regression Approach for Identifying Hot Spots in Protein Interfacesis used to derive a prediction model. To demonstrate its effectiveness, the proposed method is applied to ASEdb. Our prediction model achieves an accuracy of 0.819, F1 score of 0.743. Experimental results show that the additional features can improve the prediction performance. Especially phi-psi ha
35#
發(fā)表于 2025-3-27 15:34:57 | 只看該作者
36#
發(fā)表于 2025-3-27 19:42:37 | 只看該作者
37#
發(fā)表于 2025-3-27 22:19:57 | 只看該作者
Using Twitter Data and Sentiment Analysis to Study Diseases Dynamics information about diseases from Twitter with spatio-temporal constraints, i.e. considering a specific geographic area during a given period. We exploit the SNOMED-CT terminology to correctly detect medical terms, using sentiment analysis to assess to what extent each disease is perceived by persons
38#
發(fā)表于 2025-3-28 02:43:14 | 只看該作者
39#
發(fā)表于 2025-3-28 09:13:29 | 只看該作者
40#
發(fā)表于 2025-3-28 12:13:46 | 只看該作者
The Discovery of Prognosis Factors Using Association Rule Mining in Acute Myocardial Infarction withrch regarding the prognosis factor of acute myocardial infarction, and several previous studies has some limitations which are generation of incorrect population and potential data bias. Thus, we suggest the generation of prognosis factor based on association rule mining for acute myocardial infarct
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