AIMC Topic: Machine Learning

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Metabolic signature of COVID-19 progression: potential prognostic markers for severity and outcome.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: There are significant challenges remain in accurately categorizing the risk of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) patients.

Imbalanced feature generation based on bootstrap power spectral curve for estimating respiratory rate.

Scientific reports
Rapid respiratory rate (RR) changes in older adults may indicate serious illness. Therefore, accurately estimating RR for cardiorespiratory fitness is essential. However, machine learning algorithm-related errors are unsuitable for medical decision-m...

Cultural differences in the beauty premium.

Scientific reports
A large body of research suggests that better-looking people are associated with various positive outcomes, an effect labeled the beauty premium. However, the vast majority of this evidence has been demonstrated in WEIRD (Western, Educated, Industria...

SLIMBRAIN database: A multimodal image database of in vivo human brains for tumour detection.

Scientific data
Hyperspectral imaging (HSI) and machine learning (ML) have been employed in the medical field for classifying highly infiltrative brain tumours. Although existing HSI databases of in vivo human brains are available, they present two main deficiencies...

Deciphering the history of ERK activity from fixed-cell immunofluorescence measurements.

Nature communications
The RAS/ERK pathway plays a central role in diagnosis and therapy for many cancers. ERK activity is highly dynamic within individual cells and drives cell proliferation, metabolism, and other processes through effector proteins including c-Myc, c-Fos...

A new method for drinking water quality risk assessment based on data-driven.

Environmental geochemistry and health
Risk assessment of water quality plays a crucial role in sustainable management of water resource. However, evaluating drinking water quality risk for different types of water within the same framework is a challenging task. The Water Quality Index (...

Non-invasive prediction of DCE-MRI radiomics model on CCR5 in breast cancer based on a machine learning algorithm.

Cancer biomarkers : section A of Disease markers
BackgroundNon-invasive methods with universal prognostic guidance for detecting breast cancer (BC) survival biomarkers need to be further explored.ObjectiveThis study aimed to investigate C-C motif chemokine receptor type 5 (CCR5) prognosis value in ...

Exploring Hidden Dangers: Predicting Mycotoxin-like Toxicity and Mapping Toxicological Networks in Hepatocellular Carcinoma.

Journal of chemical information and modeling
Mycotoxins are potent triggers of hepatocellular carcinoma (HCC) due to their intricate interplay with cellular macromolecules and signaling pathways. This study integrates machine learning and biomolecular analyses to elucidate the mechanisms underl...

Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training.

Sensors (Basel, Switzerland)
This study explores nursing students' stress responses while they are being trained in a mixed reality (MR) setting that replicates highly stressful clinical scenarios. Using measurements of physiological indices such as heart rate, electrodermal act...

Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions.

Sensors (Basel, Switzerland)
Continuous monitoring of glucose levels is important for diabetes management and prevention. While traditional glucose monitoring methods are often invasive and expensive, recent approaches using machine learning (ML) models have explored non-invasiv...