AIMC Topic: Machine Learning

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How accurate are machine learning models in predicting anti-seizure medication responses: A systematic review.

Epilepsy & behavior : E&B
IMPORTANCE: Current epilepsy management protocols often depend on anti-seizure medication (ASM) trials and assessment of clinical response. This may delay the initiation of the ASM regimen that might optimally balance efficacy and tolerability for in...

Anxiety in aquatics: Leveraging machine learning models to predict adult zebrafish behavior.

Neuroscience
Accurate analysis of anxiety behaviors in animal models is pivotal for advancing neuroscience research and drug discovery. This study compares the potential of DeepLabCut, ZebraLab, and machine learning models to analyze anxiety-related behaviors in ...

Exploratory study of extracellular matrix biomarkers for non-invasive liver fibrosis staging: A machine learning approach with XGBoost and explainable AI.

Clinical biochemistry
BACKGROUND: Novel circulating markers for the non-invasive staging of chronic liver disease (CLD) are in high demand. Although underutilized, extracellular matrix (ECM) components offer significant diagnostic potential. This study evaluates ECM-relat...

Generative AI: driving productivity and scientific breakthroughs in pharmaceutical R&D.

Drug discovery today
The rapid advancement of generative artificial intelligence (AI) is reshaping pharmaceutical research and development (R&D), offering opportunities across drug discovery and development. Generative AI (GenAI) enhances productivity by enabling virtual...

Machine Learning-Assisted "Shrink-Restricted" SERS Strategy for Classification of Environmental Nanoplastic-Induced Cell Death.

Environmental science & technology
The biotoxicity of nanoplastics (NPs), especially from environmental sources, and "NPs carrier effect" are in the early stages of research. This study presents a machine learning-assisted "shrink-restricted" SERS strategy (SRSS) to monitor molecular ...

Machine Learning Techniques for Sensor-Based Human Activity Recognition with Data Heterogeneity-A Review.

Sensors (Basel, Switzerland)
Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analyzing behaviors through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most stu...

Mapping Thrombosis Serum Markers by H-NMR Allied with Machine Learning Tools.

Molecules (Basel, Switzerland)
Machine learning and artificial intelligence tools were used to investigate the discriminatory potential of blood serum metabolites for thromboembolism and antiphospholipid syndrome (APS). H-NMR-based metabonomics data of the serum samples of patient...

Should Artificial Intelligence Play a Durable Role in Biomedical Research and Practice?

International journal of molecular sciences
During the last decade, artificial intelligence (AI) was applied to nearly all domains of human activity, including scientific research. It is thus warranted to ask whether AI thinking should be durably involved in biomedical research. This problem w...

Advances in Machine Learning-Aided Thermal Imaging for Early Detection of Diabetic Foot Ulcers: A Review.

Biosensors
The prevention and early warning of foot ulcers are crucial in diabetic care; however, early microvascular lesions are difficult to detect and often diagnosed at later stages, posing serious health risks. Infrared thermal imaging, as a rapid and non-...