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Title: Abstracts.
Source: FEBS Open Bio; May2025 Supplement 1, Vol. 15, p14-58, 45p
Subject Terms: BRUTON tyrosine kinase, LANGUAGE models, B cell lymphoma, COMPUTER-assisted drug design, B cell receptors, MACHINE learning, BIG data
Abstract: The document presents a collection of research summaries from various studies focusing on advancements in health and biomedical sciences. Key topics include the identification of potential therapeutic agents for diseases such as cancer and diabetes, the application of artificial intelligence in healthcare, and the exploration of genetic factors influencing health outcomes. Notable findings include the discovery of phytochemicals as potential inhibitors for Bruton's tyrosine kinase in B cell malignancies, the development of AI-driven diagnostic tools for respiratory diseases, and the investigation of genetic polymorphisms affecting athletic performance. The research emphasizes the importance of personalized medicine, the integration of machine learning in clinical settings, and the need for further studies to validate findings and improve patient care. Overall, the document highlights innovative approaches to address pressing health challenges through interdisciplinary research and technology. [Extracted from the article]
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Database: Complementary Index
Description
Abstract:The document presents a collection of research summaries from various studies focusing on advancements in health and biomedical sciences. Key topics include the identification of potential therapeutic agents for diseases such as cancer and diabetes, the application of artificial intelligence in healthcare, and the exploration of genetic factors influencing health outcomes. Notable findings include the discovery of phytochemicals as potential inhibitors for Bruton's tyrosine kinase in B cell malignancies, the development of AI-driven diagnostic tools for respiratory diseases, and the investigation of genetic polymorphisms affecting athletic performance. The research emphasizes the importance of personalized medicine, the integration of machine learning in clinical settings, and the need for further studies to validate findings and improve patient care. Overall, the document highlights innovative approaches to address pressing health challenges through interdisciplinary research and technology. [Extracted from the article]
ISSN:22115463
DOI:10.1002/2211-5463.70030