AIMC Topic: Machine Learning

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Molecular mechanisms of lipid metabolism abnormalities driving sepsis and atrial fibrillation: A Systematic study based on bioinformatics and machine learning.

PloS one
BACKGROUND: Sepsis and atrial fibrillation are complex, life-threatening medical conditions affecting approximately 49 million individuals globally, characterized by exceptionally high mortality rates. Lipid metabolism abnormalities play a critical r...

Building extraction from remote sensing imagery using SegFormer with post-processing optimization.

PloS one
Traditional methods for building extraction from remote sensing images rely on feature classification techniques, which often suffer from high usage thresholds, cumbersome data processing, slow recognition speeds, and poor adaptability. With the rapi...

Flight delay prediction: Evaluating machine learning algorithms for enhanced accuracy.

PloS one
Flight delays pose substantial operational and economic challenges for airlines, directly affecting scheduling efficiency, resource allocation, and passenger satisfaction. Accurate prediction of arrival delays is therefore critical for optimizing air...

COMET: A Machine-Learning Framework Integrating Ligand-Based and Target-Based Algorithms for Elucidating Drug Targets.

Journal of medicinal chemistry
Elucidation of the potential molecular targets of a bioactive compound, a process known as target-fishing, is a critical task in drug discovery. Computational methods can efficiently narrow down the candidate targets for subsequent experimental valid...

Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning.

European journal of medical research
BACKGROUND: Age-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unval...

Artificial intelligence revolutionize food detection? Vision, olfaction and taste integrated with machine learning/deep learning in food detection.

Food chemistry
The rapid advancement of artificial intelligence (AI) is profoundly transforming the theoretical framework and technological paradigm of food detection. The study focuses on elucidating the underlying mechanisms of machine learning (ML)- and deep lea...

Integrated m6A reader network in acute myeloid leukemia: prognostic modeling, immune modulation, and functional validation of YTHDF3.

International immunopharmacology
Emerging evidence highlights RNA N6-methyladenosine (m6A) modifications as pivotal regulators of tumorigenesis, yet the synergistic roles of m6A readers in the pathogenesis and prognosis of acute myeloid leukemia (AML) remains unclear. By leveraging ...

A Standardized Benchmark for Machine-Learned Molecular Dynamics Using Weighted Ensemble Sampling.

The journal of physical chemistry. B
The rapid evolution of molecular dynamics (MD) methods, including machine-learned dynamics, has outpaced the development of standardized tools for method validation. Objective comparison between simulation approaches is often hindered by inconsistent...

Reticular-Induced Energy Transfer Driven Renewable ECL System with Machine Learning for Glioma-Specific Dual-Biomarker Detection and Expression Correlation Mechanism.

Analytical chemistry
Rapid, accurate, and renewable electrochemiluminescence (ECL) bioassays are crucial for multiplexed biomarker detection. Integrated with efficient analytical model for processing sensing data, these tools enable precise differentiation of tumor stage...

An explainable machine learning-based approach to predicting treatment response for neurofeedback in ADHD.

Scientific reports
Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with serious long-term effects if untreated, emphasizing the need for early treatment given its neurobiological heterogeneity. This study introduces a novel expla...