AIMC Topic: Machine Learning

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Enhancing motor imagery EEG signal decoding through machine learning: A systematic review of recent progress.

Computers in biology and medicine
This systematic literature review explores the intersection of neuroscience and deep learning in the context of decoding motor imagery Electroencephalogram (EEG) signals to enhance the quality of life for individuals with motor disabilities. Currentl...

Exploring diabetes through the lens of AI and computer vision: Methods and future prospects.

Computers in biology and medicine
Early diagnosis and timely initiation of treatment plans for diabetes are crucial for ensuring individuals' well-being. Emerging technologies like artificial intelligence (AI) and computer vision are highly regarded for their ability to enhance the a...

Hybrid statistical and machine-learning approach to hearing-loss identification based on an oversampling technique.

Computers in biology and medicine
BACKGROUND AND OBJECTIVES: Hearing loss is a crucial global health hazard exerting considerable social and physiological effects on spoken language and cognition. Patients affected by this condition may experience social and professional hardships th...

Photoplethysmography as a noninvasive surrogate for microneurography in measuring stress-induced sympathetic nervous activation - A machine learning approach.

Computers in biology and medicine
The sympathetic nervous system (SNS) is essential for the body's immediate response to stress, initiating physiological changes that can be measured through sympathetic nerve activity (SNA). While microneurography (MNG) is the gold standard for direc...

S-PLM: Structure-Aware Protein Language Model via Contrastive Learning Between Sequence and Structure.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Proteins play an essential role in various biological and engineering processes. Large protein language models (PLMs) present excellent potential to reshape protein research by accelerating the determination of protein functions and the design of pro...

Novel machine learning model for predicting cancer drugs' susceptibilities and discovering novel treatments.

Journal of biomedical informatics
BACKGROUND AND OBJECTIVE: Timely treatment is crucial for cancer patients, so it's important to administer the appropriate treatment as soon as possible. Because individuals can respond differently to a given drug due to their unique genomic profiles...

Modeling the global ocean distribution of dissolved cadmium based on machine learning-SHAP algorithm.

The Science of the total environment
Cadmium (Cd) is a bio-essential trace metal in the ocean that can be toxic at high concentrations, significantly impacting the marine environment and phytoplankton growth. Its distribution pattern is closely proportional to that of phosphate (PO), al...

Toward Dose Prediction at Point of Design.

Journal of medicinal chemistry
Human dose prediction (HDP) is a useful tool for compound optimization in preclinical drug discovery. We describe here our exclusively in silico HDP strategy to triage compound designs for synthesis and experimental profiling. Our goal is a model tha...

Bioinspired Iron Porphyrin Covalent Organic Frameworks-Based Nanozymes Sensor Array: Machine Learning-Assisted Identification and Detection of Thiols.

ACS applied materials & interfaces
Given the crucial role of thiols in maintaining normal physiological functions, it is essential to establish a high-throughput and sensitive analytical method to identify and quantify various thiols accurately. Inspired by the iron porphyrin active c...

Machine Learning-Assisted Liquid Crystal Optical Sensor Array Using Cysteine-Functionalized Silver Nanotriangles for Pathogen Detection in Food and Water.

ACS applied materials & interfaces
The challenge of rapid identification of bacteria in food and water still persists as a major health problem. To tackle this matter, we have developed a single-probe liquid crystal (LC)-based optical sensing platform for the differentiation of five c...