AIMC Topic: Humans

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Deep-learning analysis of greenspace and metabolic syndrome: A street-view and remote-sensing approach.

Environmental research
Evidence linking greenspace exposure to metabolic syndrome (MetS) remains sparse and inconsistent. This exploratory study evaluate the relationship between green visibility index (GVI) and normalized difference vegetation index (NDVI) with MetS preva...

A new strategy to HER2-specific antibody discovery through artificial intelligence-powered phage display screening based on the Trastuzumab framework.

Biochimica et biophysica acta. Molecular basis of disease
Human epidermal growth factor receptor 2 (HER2) is a recognized drug target, and it serves as a critical target for various cancer treatments, necessitating the discovery of more antibodies for therapeutic and detection purposes. Here, we have develo...

Development of disease diagnosis technology based on coattention cross-fusion of multiomics data.

Analytica chimica acta
BACKGROUND: Early diagnosis is vital for increasing the rates of curing diseases and patient survival in medicine. With the advancement of biotechnology, the types of bioomics data are increasing. The integration of multiomics data can provide more c...

MCDGLN: Masked connection-based dynamic graph learning network for autism spectrum disorder.

Brain research bulletin
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by complex physiological processes. Previous research has predominantly focused on static cerebral interactions, often neglecting the brain's dynamic nature and the challen...

Integrating local and global attention mechanisms for enhanced oral cancer detection and explainability.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Early detection of Oral Squamous Cell Carcinoma (OSCC) improves survival rates, but traditional diagnostic methods often produce inconsistent results. This study introduces the Oral Cancer Attention Network (OCANet), a U-Net...

Female autism categorization using CNN based NeuroNet57 and ant colony optimization.

Computers in biology and medicine
Autism identification and classification using biomedical medical image analysis has advanced recently. Research shows autistic females have different phenotypic and age-related brain variations than males. Gender-specific hormones and genes affect a...

Automatic cerebral microbleeds detection from MR images via multi-channel and multi-scale CNNs.

Computers in biology and medicine
BACKGROUND: Computer-aided detection (CAD) systems have been widely used to assist medical professionals in interpreting medical images, aiding in the detection of potential diseases. Despite their usefulness, CAD systems cannot yet fully replace doc...

A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations.

Nature human behaviour
This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals acr...

Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major osteoporotic fractures in older men. The Super Learner (SL) model achieved superior performance (AU...

Interpretable Multimodal Fusion Model for Bridged Histology and Genomics Survival Prediction in Pan-Cancer.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Understanding the prognosis of cancer patients is crucial for enabling precise diagnosis and treatment by clinical practitioners. Multimodal fusion models based on artificial intelligence (AI) offer a comprehensive depiction of the tumor heterogeneit...