AIMC Topic: Machine Learning

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Machine Learning-Enhanced Chemiresistive Sensors for Ultra-Sensitive Detection of Methanol Adulteration in Alcoholic Beverages.

ACS sensors Jun 11, 2025
Methanol poisoning poses significant health risks, particularly in less developed countries, where adulterated alcoholic beverages often lead to severe morbidity and mortality. Current diagnostic methods, such as gas-liquid chromatography and blood g...
Machine Learning Food Contamination Methanol Graphite Alcoholic Beverages Limit of Detection Metal-Organic Frameworks Principal Component Analysis
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Multivariate and Machine Learning-Derived Virtual Staining and Biochemical Quantification of Cancer Cells through Raman Hyperspectral Imaging.

Analytical chemistry Jun 11, 2025
Advances in virtual staining and spatial omics have revolutionized our ability to explore cellular architecture and molecular composition with unprecedented detail. Virtual staining techniques, which rely on computational algorithms to map molecular ...
Algorithms Cell Line, Tumor Staining and Labeling Neural Networks, Computer Machine Learning Humans Hyperspectral Imaging Multivariate Analysis Principal Component Analysis Least-Squares Analysis Collagen Spectrum Analysis, Raman Lipids
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Robust Multiclass Feature Selection for the Authentication of Honey Botanical Origin via Nontargeted LC-MS Analysis.

Analytical chemistry Jun 11, 2025
Honey is one of the most frequently frauded foods due to the high market price of certain kinds of monofloral honey. Traditional authentication methods involving pollen or targeted analysis have limitations that can be manipulated by fraudsters. Nont...
Honey Chromatography, Liquid Liquid Chromatography-Mass Spectrometry Support Vector Machine Mass Spectrometry Machine Learning
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Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state.

Science advances Jun 11, 2025
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively pertur...
Alzheimer Disease Protein Aggregates Thermodynamics Mutation Kinetics Humans Amyloid beta-Peptides Peptide Fragments Machine Learning
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Diagnostic accuracy of machine learning-based magnetic resonance imaging models in breast cancer classification: a systematic review and meta-analysis.

World journal of surgical oncology Jun 11, 2025
OBJECTIVE: This meta-analysis evaluates the diagnostic accuracy of machine learning (ML)-based magnetic resonance imaging (MRI) models in distinguishing benign from malignant breast lesions and explores factors influencing their performance.
Magnetic Resonance Imaging Female Humans Sensitivity and Specificity Machine Learning Breast Neoplasms
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28-day all-cause mortality in patients with alcoholic cirrhosis: a machine learning prediction model based on the MIMIC-IV.

Clinical and experimental medicine Jun 11, 2025
To develop and validate a machine learning prediction model for 28-day all-cause mortality in patients with alcoholic cirrhosis using data from the MIMIC-IV database. The data of 2134 patients diagnosed with alcoholic cirrhosis (AC) were obtained fro...
Male Humans Middle Aged ROC Curve Machine Learning Female Aged Liver Cirrhosis, Alcoholic Risk Factors Adult
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Next-Generation Sequencing-Based Testing Among Patients With Advanced or Metastatic Nonsquamous Non-Small Cell Lung Cancer in the United States: Predictive Modeling Using Machine Learning Methods.

JMIR cancer Jun 11, 2025
BACKGROUND: Next-generation sequencing (NGS) has become a cornerstone of treatment for lung cancer and is recommended in current treatment guidelines for patients with advanced or metastatic disease.
United States Carcinoma, Non-Small-Cell Lung High-Throughput Nucleotide Sequencing Male Middle Aged Machine Learning Lung Neoplasms Female Aged Adult Humans Biomarkers, Tumor
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Preparing Tomorrow's Physicians: The Case for Machine Learning in Medical Education.

Journal of medical systems Jun 11, 2025
Machine learning should be integrated into medical curricula to prepare physicians-in-training for 21st-century practice conditions. This comment proposes practical implementation strategies that build upon existing educational frameworks by drawing ...
Humans Machine Learning Curriculum Education, Medical
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POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.

Journal of neural engineering Jun 11, 2025
. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it has shortcomings including sensitivity to noise and rigidity in the value of the weights. Our goal wa...
Male Humans Neural Networks, Computer Electroencephalography Imagination Adult Machine Learning Brain-Computer Interfaces
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Enhancing R-loop prediction with high-throughput sequencing data.

NAR genomics and bioinformatics Jun 11, 2025
R-loops are three-stranded RNA and DNA hybrid structures that often occur in the genome and play important roles in a variety of cellular processes from bacteria to mammals. Sequencing methods profiling R-loops genome-wide have revealed that they can...
R-Loop Structures Mice High-Throughput Nucleotide Sequencing Chromatin Machine Learning Animals Humans
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