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| http://dx.doi.org/10.1007/s00415-021-10696-2 | DOI Listing |
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MMFuncPhos: A Multi-Modal Learning Framework for Identifying Functional Phosphorylation Sites and Their Regulatory Types.
Adv Sci (Weinh)
January 2025
Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100871, China.
Protein phosphorylation plays a crucial role in regulating a wide range of biological processes, and its dysregulation is strongly linked to various diseases. While many phosphorylation sites have been identified so far, their functionality and regulatory effects are largely unknown. Here, a deep learning model MMFuncPhos, based on a multi-modal deep learning framework, is developed to predict functional phosphorylation sites.
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View Article and Find Full Text PDFPre-patellar Tumoral Calcinosis of Knee with Intra-articular Extension: An Index Case Study.
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Department of Orthopaedic Surgery, Maulana Azad Medical College, Bahadur Shah Marg, New Delhi, India.
Introduction: Tumoral calcinosis is a rare hereditary condition characterized by the deposition of calcium phosphate and hydroxyapatite in periarticular soft tissues. First described by Giard and Duret in 1898 and later detailed by Inclan in 1943, this condition has often been confused with other forms of periarticular calcification. Tumoral calcinosis predominantly affects young males and is typically found around major joints, such as the shoulder, elbow, hip, ankle, and wrist.
View Article and Find Full Text PDFMulti-modal prediction of extracorporeal support-a resource intensive therapy, utilizing a large national database.
JAMIA Open
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Artificial Intelligence (AI) for Health Institute (AIHealth), Washington University in St Louis, St Louis, MO 63130, United States.
Objective: Extracorporeal membrane oxygenation (ECMO) is among the most resource-intensive therapies in critical care. The COVID-19 pandemic highlighted the lack of ECMO resource allocation tools. We aimed to develop a continuous ECMO risk prediction model to enhance patient triage and resource allocation.
View Article and Find Full Text PDFEnterprise chart question and answer method based on multi modal cross fusion.
Sci Rep
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Chongqing Vocational Institute of Tourism, Chongqing, China.
To enhance enterprises' interactive exploration capabilities for unstructured chart data, this paper proposes a multimodal chart question-answering method. Facing the challenge of recognizing curved and irregular text in charts, we introduce Gaussian heatmap encoding technology to achieve character-level precise text annotation. Additionally, we combine a key point detection algorithm to extract numerical information from the charts and convert it into structured table data.
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