AIMC Topic: Machine Learning

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Maturation of Neuronal Activity in the Human Cortex Exhibits Robust Spatial Gradients across the Birth Transition.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Early structural and molecular development of the human cortex is extensively studied, but little is known about the development of neuronal activity across cortical regions. We used dense array electroencephalography recordings and a machine learnin...

Early warning of regime switching in a financial time series: A heteroskedastic network model.

PloS one
Regime switching in a time series is an important and challenging issue in complex financial system analysis. Existing regime models have focused on the features of fluctuations at a single point in financial time series, often neglecting time series...

Construction and application of machine learning models for predicting intradialytic hypotension.

PloS one
INTRODUCTION: Intradialytic hypotension (IDH) remains a prevalent complication of hemodialysis, which is associated with adverse outcomes for patients. This study seeks to harness machine learning to construct predictive models for IDH based on multi...

Machine learning-based forecasting of air quality index under long-term environmental patterns: A comparative approach with XGBoost, LightGBM, and SVM.

PloS one
Air pollution is a global problem that threatens environmental sustainability and severely affects public health. Monitoring air quality and predicting future pollution levels are critical for creating effective environmental policies and enabling in...

Prediction of changes in suitable habitats for tea plants in China's four major tea-producing regions based on machine learning models.

PloS one
Under the background of ongoing global climate warming, clarifying the spatiotemporal dynamics of suitable habitats for tea plants and their potential impact on forest ecosystems is essential for promoting sustainable tea industry development and eco...

Robust detection of femtogram-level Alzheimer's biomarkers using machine learning-enhanced graphene biosensors.

Biosensors & bioelectronics
Early diagnosis of Alzheimer's disease (AD) requires blood biomarker tests sensitive to femtogram/mL concentrations. Graphene field-effect transistors (GFETs) are promising for this application, but suffer from device-to-device variability and requir...

Harnessing machine learning for metagenomic data analysis: trends and applications.

mSystems
Metagenomic sequencing has revolutionized our understanding of microbial ecosystems by enabling high-resolution profiling of microbes across diverse environments. However, the resulting data are high-dimensional, sparse, and noisy, posing challenges ...

Discovering Biomarkers for Asymptomatic Tuberculosis via Olink Proteomics and Machine Learning.

Journal of proteome research
The diagnosis of asymptomatic tuberculosis (TB) remains challenging due to an early disease stage. This study aimed to identify and validate plasma biomarkers for asymptomatic TB by integrating the Olink proteomics with multiple machine learning algo...

Neutrophil extracellular trapping network-associated biomarkers in liver fibrosis: machine learning and experimental validation.

Journal of translational medicine
BACKGROUND: The diagnostic and therapeutic potential of neutrophil extracellular traps (NETs) in liver fibrosis (LF) has not been fully explored. We aim to screen and verify NETs-related liver fibrosis biomarkers through machine learning.