AIMC Topic: Machine Learning

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Clinical impact of an explainable machine learning with amino acid PET imaging: application to the diagnosis of aggressive glioma.

European journal of nuclear medicine and molecular imaging
PURPOSE: Radiomics-based machine learning (ML) models of amino acid positron emission tomography (PET) images have shown efficiency in glioma prediction tasks. However, their clinical impact on physician interpretation remains limited. This study inv...

Machine learning-integrated droplet microfluidic system for accurate quantification and classification of microplastics.

Water research
Microplastic (MP) pollution poses serious environmental and public health concerns, requiring efficient detection methods. Conventional techniques have the limitations of labor-intensive workflows and complex instrumentation, hindering rapid on-site ...

Exposure experiments and machine learning revealed that personal care products can significantly increase transdermal exposure of SVOCs from the environment.

Journal of hazardous materials
We investigated the impacts of personal care products (PCPs) on dermal exposure to semi-volatile organic compounds (SVOCs), including phthalates, organophosphate esters, polycyclic aromatic hydrocarbons (PAHs), ultraviolet filters, and p-phenylenedia...

Bimodal In Situ Analyzer for Circular RNA in Extracellular Vesicles Combined with Machine Learning for Accurate Gastric Cancer Detection.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Circular RNAs in extracellular vesicles (EV-circRNAs) are gaining recognition as potential biomarkers for the diagnosis of gastric cancer (GC). Most current research is focused on identifying new biomarkers and their functional significance in diseas...

Exploring the impact of natural and human activities on vegetation changes: An integrated analysis framework based on trend analysis and machine learning.

Journal of environmental management
Climate, human activities and terrain are crucial factors influencing vegetation changes. Despite their crucial role, there is a notable lack of research exploring the nonlinear relationships between them and vegetation changes, especially over exten...

Machine learning-based models for prediction of innovative medicine reimbursement decisions in Scotland.

Journal of epidemiology and population health
OBJECTIVE: This study aimed to investigate the critical factors for reimbursement decisions of innovative medicines in Scotland and to explore the feasibility of machine learning models for predicting decisions.

Bacterial Wastewater-Based Epidemiology Using Surface-Enhanced Raman Spectroscopy and Machine Learning.

Nano letters
Although wastewater-based epidemiology has been used extensively for the surveillance of viral diseases, it has not been used to a similar extent for bacterial diseases. This is in part owing to difficulties in distinguishing pathogenic from nonpatho...

Cellular Senescence in Hepatocellular Carcinoma: Immune Microenvironment Insights via Machine Learning and In Vitro Experiments.

International journal of molecular sciences
Hepatocellular carcinoma (HCC), a leading liver tumor globally, is influenced by diverse risk factors. Cellular senescence, marked by permanent cell cycle arrest, plays a crucial role in cancer biology, but its markers and roles in the HCC immune mic...

Structure-Based Approaches for Protein-Protein Interaction Prediction Using Machine Learning and Deep Learning.

Biomolecules
Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-ba...

Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and fatty acid metabolism-associated gene.

Journal of cardiothoracic surgery
BACKGROUND: Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and acts as a major contributor to cardiovascular diseases. Advancements in lipidomics and metabolomics have ...