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MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.
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School of Computer, Guangdong University of Education, Guangzhou 510310, China.
Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel drug-target prediction model called MCF-DTI. The model utilizes the SMILES representation of drugs and the sequence features of targets, employing a multi-scale convolutional neural network (MSCNN) with parallel shared-weight modules to extract features from the drug side.
View Article and Find Full Text PDFVirtual patients using large language models: Scalable, contextualized simulation of clinician-patient dialog with feedback.
J Med Internet Res
January 2025
Division of General Internal Medicine, Mayo Clinic College of Medicine and Science, 200 First St SW, Rochester, US.
Background: Virtual patients (VPs) are computer screen-based simulations of patient-clinician encounters. VP use is limited by cost and low scalability.
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Analyzing Predictive Factors for the Media's Impact on the Nursing Profession.
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Department of Nursing, University of Applied Health Sciences, 10000 Zagreb, Croatia.
Mass media play a crucial role not only in informing the public but also in shaping public perception, educating, and enhancing the visibility of various professions, including nursing. Despite being the most populous healthcare profession, nursing remains underrepresented in media coverage. This imbalance affects the social status of the nursing profession and its public perception.
View Article and Find Full Text PDFTechnologies for Interoperable Internet of Medical Things Platforms to Manage Medical Emergencies in Home and Prehospital Care: Scoping Review.
J Med Internet Res
January 2025
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Background: The aging global population and the rising prevalence of chronic disease and multimorbidity have strained health care systems, driving the need for expanded health care resources. Transitioning to home-based care (HBC) may offer a sustainable solution, supported by technological innovations such as Internet of Medical Things (IoMT) platforms. However, the full potential of IoMT platforms to streamline health care delivery is often limited by interoperability challenges that hinder communication and pose risks to patient safety.
View Article and Find Full Text PDFMVSLLnc: LncRNA subcellular localization prediction based on multi-source features and two-stage voting strategy.
Methods
January 2025
National Center for Applied Mathematics in Hunan, Xiangtan University, Hunan 411105, China; Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, Hunan 411105, China. Electronic address:
The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding the function of lncRNAs. Since the traditional biological experimental methods are time-consuming and some existing computational methods rely on high computing power, we are committed to finding a simple and easy-to-implement method to achieve more efficient prediction of the subcellular localization of lncRNAs. In this work, we proposed a model based on multi-source features and two-stage voting strategy for predicting the subcellular localization of lncRNAs (MVSLLnc).
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