AIMC Topic: Machine Learning

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Computed tomography-based radiomics machine learning models for differentiating enchondroma and atypical cartilaginous tumor in long bones.

RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
To explore the value of CT-based radiomics machine learning models for differentiating enchondroma from atypical cartilaginous tumor (ACT) in long bones and methods to improve model performance.59 enchondromas and 53 ACTs in long bones confirmed by p...

The Relationship Between Metal Exposure and HPV Infection: Evidence from Explainable Machine Learning Methods.

Biological trace element research
HPV is a ubiquitous pathogen implicated in cervical and other cancers. Although vaccines are available, they do not encompass all subtypes. Meanwhile, metal exposure may elevate the risk of HPV infection and amplify its carcinogenic potential, but st...

Adsorption behavior and mechanism of heavy metals onto microplastics: A meta-analysis assisted by machine learning.

Environmental pollution (Barking, Essex : 1987)
Microplastics (MPs) have the potential to adsorb heavy metals (HMs), resulting in a combined pollution threat in aquatic and terrestrial environments. However, due to the complexity of MP/HM properties and experimental conditions, research on the ads...

Predicting inferior vena cava filter complications using machine learning.

Journal of vascular surgery. Venous and lymphatic disorders
OBJECTIVE: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (M...

Utilizing machine learning and bioinformatics analysis to identify drought-responsive genes affecting yield in foxtail millet.

International journal of biological macromolecules
Drought stress is a major constraint on crop development, potentially causing huge yield losses and threatening global food security. Improving Crop's stress tolerance is usually associated with a yield penalty. One way to balance yield and stress to...

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota.

Environmental management
The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in miti...

Machine-learning and scRNA-Seq-based diagnostic and prognostic models illustrating survival and therapy response of lung adenocarcinoma.

Genes and immunity
Lung cancer is a major cause accounting for cancer-related mortalities, with lung adenocarcinoma (LUAD) being the most prevalent subtype. Given the high clinical and cellular heterogeneities of LUAD, accurate diagnosis and prognosis are crucial to av...

An abbreviated Chinese dyslexia screening behavior checklist for primary school students using a machine learning approach.

Behavior research methods
To increase early identification and intervention of dyslexia, a prescreening instrument is critical to identifying children at risk. The present work sought to shorten and validate the 30-item Mandarin Dyslexia Screening Behavior Checklist for Prima...

Machine Learning Strategies to Tackle Data Challenges in Mass Spectrometry-Based Proteomics.

Journal of the American Society for Mass Spectrometry
In computational proteomics, machine learning (ML) has emerged as a vital tool for enhancing data analysis. Despite significant advancements, the diversity of ML model architectures and the complexity of proteomics data present substantial challenges...

Exploring Machine Learning Algorithms to Predict Diarrhea Disease and Identify its Determinants among Under-Five Years Children in East Africa.

Journal of epidemiology and global health
BACKGROUND: The second most common cause of death for children under five is diarrhea. Early Predicting diarrhea disease and identify its determinants (factors) using an advanced machine learning model is the most effective way to save the lives of c...