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Clinical Decision Support Using Speech Signal Analysis: Systematic Scoping Review of Neurological Disorders.
J Med Internet Res
January 2025
Knight Foundation of Computing & Information Sciences, Florida International University, Miami, FL, United States.
Background: Digital biomarkers are increasingly used in clinical decision support for various health conditions. Speech features as digital biomarkers can offer insights into underlying physiological processes due to the complexity of speech production. This process involves respiration, phonation, articulation, and resonance, all of which rely on specific motor systems for the preparation and execution of speech.
View Article and Find Full Text PDFIdentification of Use Cases, Target Groups, and Motivations Around Adopting Smart Speakers for Health Care and Social Care Settings: Scoping Review.
JMIR AI
January 2025
Faculty of Social Science, Ruhr University Bochum, Bochum, Germany.
Background: Conversational agents (CAs) are finding increasing application in health and social care, not least due to their growing use in the home. Recent developments in artificial intelligence, machine learning, and natural language processing have enabled a variety of new uses for CAs. One type of CA that has received increasing attention recently is smart speakers.
View Article and Find Full Text PDFPHIStruct: Improving phage-host interaction prediction at low sequence similarity settings using structure-aware protein embeddings.
Bioinformatics
January 2025
Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, De La Salle University, Manila, 1004, Philippines.
Motivation: Recent computational approaches for predicting phage-host interaction have explored the use of sequence-only protein language models to produce embeddings of phage proteins without manual feature engineering. However, these embeddings do not directly capture protein structure information and structure-informed signals related to host specificity.
Results: We present PHIStruct, a multilayer perceptron that takes in structure-aware embeddings of receptor-binding proteins, generated via the structure-aware protein language model SaProt, and then predicts the host from among the ESKAPEE genera.
EnrichRBP: an automated and interpretable computational platform for predicting and analyzing RNA-binding protein events.
Bioinformatics
January 2025
School of Artificial Intelligence, Jilin University, Jilin, China.
Motivation: Predicting RNA-binding proteins (RBPs) is central to understanding post-transcriptional regulatory mechanisms. Here, we introduce EnrichRBP, an automated and interpretable computational platform specifically designed for the comprehensive analysis of RBP interactions with RNA.
Results: EnrichRBP is a web service that enables researchers to develop original deep learning and machine learning architectures to explore the complex dynamics of RNA-binding proteins.
Single Extracellular VEsicle Nanoscopy-Universal Protocol (SEVEN-UP): Accessible Imaging Platform for Quantitative Characterization of Single Extracellular Vesicles.
Anal Chem
January 2025
Department of Cancer Biology and Molecular Medicine, Beckman Research Institute, City of Hope Comprehensive Cancer Center, Duarte, California 91010, United States.
Extracellular vesicles (EVs), membrane-encapsulated nanoparticles shed from all cells, are tightly involved in critical cellular functions. Moreover, EVs have recently emerged as exciting therapeutic modalities, delivery vectors, and biomarker sources. However, EVs are difficult to characterize, because they are typically small and heterogeneous in size, origin, and molecular content.
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