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Multimodal cross-scale context clusters for classification of mental disorders using functional and structural MRI.
Neural Netw
January 2025
The Key Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu, Sichuan 610225, China. Electronic address:
The brain is a complex system with multiple scales and hierarchies, making it challenging to identify abnormalities in individuals with mental disorders. The dynamic segregation and integration of activities across brain regions enable flexible switching between local and global information processing modes. Modeling these scale dynamics within and between brain regions can uncover hidden correlates of brain structure and function in mental disorders.
View Article and Find Full Text PDFApplication of elastic net for clinical outcome prediction and classification in progressive supranuclear palsy: A multicenter cohort study.
Parkinsonism Relat Disord
January 2025
Department of Neurology, Seoul Metropolitan Government-Seoul National University Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea. Electronic address:
Background: Previous studies have used machine learning to identify clinically relevant atrophic regions in progressive supranuclear palsy (PSP). This study applied Elastic Net (EN) in PSP to uncover key atrophic patterns, offering a novel approach to understanding its pathology.
Methods: This study included baseline data from 74 patients with PSP enrolled in the Study of Comprehensive ANd multimodal marker-based cohort of PSP (SCAN-PSP, NCT05579301) in Seoul between January 2022 and August 2023.
A Multidisciplinary Multimodal Aligned Dataset for Academic Data Processing.
Sci Data
January 2025
Shanghai Artificial Intelligence Research Institute Co., Ltd., Shanghai, 200240, China.
Academic data processing is crucial in scientometrics and bibliometrics, such as research trending analysis and citation recommendation. Existing datasets in this domain have predominantly concentrated on textual data, overlooking the importance of visual elements. To bridge this gap, we introduce a multidisciplinary multimodal aligned dataset (MMAD) specifically designed for academic data processing.
View Article and Find Full Text PDFSSL-VQ: Vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases.
Bioinformatics
January 2025
Department of Bioscience and Bioinformatics, Faculty of Computer Science and Systems Engineering, Kyushu Institute of Technology, Iizuka, Fukuoka 820-8502, Japan.
Motivation: Identifying effective therapeutic targets poses a challenge in drug discovery, especially for uncharacterized diseases without known therapeutic targets (e.g. rare diseases, intractable diseases).
View Article and Find Full Text PDFA deep-learning system integrating electrocardiograms and laboratory indicators for diagnosing acute aortic dissection and acute myocardial infarction.
Int J Cardiol
January 2025
Department of Computer Center, Zigong Fourth People's Hospital, Zigong, Sichuan 643000, China.
Background: Acute Stanford Type A aortic dissection (AAD-type A) and acute myocardial infarction (AMI) present with similar symptoms but require distinct treatments. Efficient differentiation is critical due to limited access to radiological equipment in many primary healthcare. This study develops a multimodal deep learning model integrating electrocardiogram (ECG) signals and laboratory indicators to enhance diagnostic accuracy for AAD-type A and AMI.
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