AIMC Topic: Machine Learning

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Machine Learning Models as Early Warning Systems for Neonatal Infection.

Clinics in perinatology
Neonatal infections pose a significant threat to the health of newborns. Associated morbidity and mortality risks underscore the urgency of prompt diagnosis and treatment with appropriate empiric antibiotics. Delay in treatment can be fatal; thus, ea...

Thickness Speed Progression Index: Machine Learning Approach for Keratoconus Detection.

American journal of ophthalmology
PURPOSE: To develop and validate a pachymetry-based machine learning (ML) index for differentiating keratoconus, keratoconus suspect, and normal corneas.

Gross primary productivity estimation through remote sensing and machine learning techniques in the high Andean Region of Ecuador.

International journal of biometeorology
Accurately estimating gross primary productivity (GPP) is crucial for simulating the carbon cycle and addressing the challenges of climate change. However, estimating GPP is challenging due to the absence of direct measurements at scales larger than ...

CLHGNNMDA: Hypergraph Neural Network Model Enhanced by Contrastive Learning for miRNA-Disease Association Prediction.

Journal of computational biology : a journal of computational molecular cell biology
Numerous biological experiments have demonstrated that microRNA (miRNA) is involved in gene regulation within cells, and mutations and abnormal expression of miRNA can cause a myriad of intricate diseases. Forecasting the association between miRNA an...

Machine learning-enhanced multi-trait genomic prediction for optimizing cannabinoid profiles in cannabis.

The Plant journal : for cell and molecular biology
Cannabis sativa L., known for its medicinal and psychoactive properties, has recently experienced rapid market expansion but remains understudied in terms of its fundamental biology due to historical prohibitions. This pioneering study implements GS ...

Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model.

Acta obstetricia et gynecologica Scandinavica
INTRODUCTION: Induction of labor, often used for pregnancy termination, has globally rising rates, especially in high-income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean sectio...

Machine-Learning Mental-Fatigue-Measuring μm-Thick Elastic Epidermal Electronics (MMMEEE).

Nano letters
Electrophysiological (EP) signals are key biomarkers for monitoring mental fatigue (MF) and general health, but state-of-the-art wearable EP-based MF monitoring systems are bulky and require user-specific, labeled data. Ultrathin epidermal electrodes...

Machine Learning-Driven Discovery and Database of Cyanobacteria Bioactive Compounds: A Resource for Therapeutics and Bioremediation.

Journal of chemical information and modeling
Cyanobacteria strains have the potential to produce bioactive compounds that can be used in therapeutics and bioremediation. Therefore, compiling all information about these compounds to consider their value as bioresources for industrial and researc...

Metastatic Versus Localized Disease as Inclusion Criteria That Can Be Automatically Extracted From Randomized Controlled Trials Using Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: Extracting inclusion and exclusion criteria in a structured, automated fashion remains a challenge to developing better search functionalities or automating systematic reviews of randomized controlled trials in oncology. The question "Did th...

Identification of CCR7 and CBX6 as key biomarkers in abdominal aortic aneurysm: Insights from multi-omics data and machine learning analysis.

IET systems biology
Abdominal aortic aneurysm (AAA) is a severe vascular condition, marked by the progressive dilation of the abdominal aorta, leading to rupture if untreated. The objective of this study was to identify key biomarkers and decipher the immune mechanisms ...