AIMC Topic: Machine Learning

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Feature Selection in Healthcare Datasets: Towards a Generalizable Solution.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: The increasing dimensionality of healthcare datasets presents major challenges for clinical data analysis and interpretation. This study introduces a scalable ensemble feature selection (FS) strategy optimized for multi-biom...

Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants.

NAR genomics and bioinformatics
Genetic variants can impact the structure of the corresponding protein, which can have detrimental effects on protein function. While the effect of protein-truncating variants is often easier to evaluate, most genetic variants that affect the protein...

Evaluation of antifouling surfaces using a method that employs mussel larvae settlement quantified by machine learning.

Biofouling
Antifouling coating development requires extensive performance testing. Coatings that prevent aquatic larval settlement are of interest because many forms of macrofouling begin at the larval stage. However, field testing can be time consuming and poo...

`Probabilistic ensemble learning for prediction of stroke thrombectomy outcomes from the NeuroVascular Quality Initiative-Quality Outcomes Database (NVQI-QOD) Acute Ischemic Stroke Registry.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
INTRODUCTION: Mechanical Thrombectomy (MT) is the standard of care in the interventional management of Acute Ischemic Stroke (AIS). The NVQI-QOD registry records detailed patient characteristics, pre-operative imaging, procedure metrics, and post-ope...

Machine learning-assisted tacrolimus dose optimization in childhood- onset systemic lupus erythematosus through population pharmacokinetic modeling.

Computers in biology and medicine
OBJECTIVE: This study aimed to improve treatment effectiveness in childhood-onset systemic lupus erythematosus (cSLE) by developing machine learning algorithms integrated with pharmacokinetic parameters to predict individualized tacrolimus dosing for...

Machine learning-driven optimization of arsenic phytoextraction using amendments.

Ecotoxicology and environmental safety
Exogenous amendments are crucial for enhancing the remediation efficiency of arsenic-contaminated soils by Pteris vittata. However, their effectiveness is unstable due to various factors, and neglecting their economic costs hinder broader application...

Prediction of hydrogen and methane yields from gasification of leather waste using machine learning and explainable AI: An original dataset.

Journal of environmental management
Accurately predicting syngas composition is essential for optimizing energy production and ensuring environmental sustainability. Despite the growing use of machine learning techniques in this field, publicly available datasets remain limited, and ex...

Distinguishing dominant drivers on long-term vegetation dynamics across China considering time-lag and accumulation effects using machine learning techniques.

Journal of environmental management
Accurate attribution of vegetation dynamics is essential to ensure the conservation, restoration and sustainability of terrestrial ecosystems. However, due to the time-lag and accumulation effects of vegetation responding climate change and anthropog...

Application of machine learning in microwave remediation of total petroleum hydrocarbon contaminated soil: Prediction and key factor identification.

Journal of environmental management
Microwave thermal remediation (TPH) is a promising remediation method for petroleum hydrocarbon contaminated soils due to its high energy efficiency and rapid heating capacity. However, the complexity of influencing factors and their nonlinear intera...

Genetic and molecular underpinnings of the link between rheumatoid arthritis and myasthenia gravis: Insights from GWAS and transcriptomic analyses.

Clinical rheumatology
BACKGROUND: Although studies have shown that patients with rheumatoid arthritis (RA) are at a higher risk of developing myasthenia gravis (MG), the causal relationship and shared genetic basis between these two diseases have not been fully investigat...