AIMC Topic: Machine Learning

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Prediction of Fraction Unbound in Human Plasma for Per- and Polyfluoroalkyl Substances: Evaluating Transfer Learning as an Algorithmic Solution to the Problem of Sparse Data.

Journal of chemical information and modeling
Fraction unbound in plasma () is a crucial parameter in physiologically based toxicokinetic (PBTK) models, representing the fraction of a chemical compound that is not sequestered by plasma proteins when present in the bloodstream. This is often used...

Machine learning and microfluidic integration for oocyte quality prediction.

Scientific reports
Despite advancements in in vitro fertilization (IVF) over the past 30 years, its outcome effectiveness remains low (20-40%). This study introduces a microfluidic-based machine learning framework to improve predictive accuracy in oocyte quality assess...

Machine learning in Alzheimer's disease genetics.

Nature communications
Traditional statistical approaches have advanced our understanding of the genetics of complex diseases, yet are limited to linear additive models. Here we applied machine learning (ML) to genome-wide data from 41,686 individuals in the largest Europe...

Machine learning-assisted spectroscopic methods for detecting adulteration in Barrantes wine from Folla Redonda grapes.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The present study explores the application of advanced machine learning algorithms combined with vis-NIRS and FTIR spectroscopy to detect and quantify adulteration in Barrantes wine, produced from the Folla Redonda grape, a variety exclusive to the G...

High pollution and health risk of antibiotic resistance genes in rural domestic sewage in southeastern China: A study combining national-scale distribution and machine learning.

Environmental pollution (Barking, Essex : 1987)
Rural domestic sewage has emerged as an important reservoir of antibiotic resistance genes (ARGs) under rapid urbanization, while the national-scale geographical patterns and risks of ARGs remaining unclear. We investigated ARG pollution in rural dom...

Machine learning enhanced process design in protein a chromatography.

Journal of chromatography. A
Quality by Digital Design (QbDD) employs in-silico experimentation to reduce wet-lab reliance and accelerate development. Design space identification is critical for QbDD to overcome bottlenecks and streamline process design. Traditional design space...

A new approach methodology (NAM) for carcinogenicity prediction of organic chemicals using the multiclass ARKA framework and machine-learning-based stacking regression.

Journal of hazardous materials
The accumulation of organic pollutants in the environment has significantly impacted the lives of flora and fauna, resulting in disruptions in the biological ecosystem. Carcinogenicity has been one of the most alarming adverse effects exhibited by th...

Optimizing models for the prediction of one step ahead extreme flows to wastewater treatment plants using different synthetic sampling methods.

Journal of environmental management
High-flow events that significantly impact Water Resource Recovery Facility (WRRF) operations are rare, but accurately predicting these flows could improve treatment operations. Data-driven modeling approaches could be used; however, high flow events...

Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces.

Ecotoxicology and environmental safety
Global urbanization has significantly contributed to soil contamination by heavy metals (HMs), posing serious ecological risks, particularly within urban green spaces (UGS). This study focused on UGS soils in Lanzhou, a major river-valley city in Chi...

Machine Learning-Enhanced Single-Particle Tracking for Rapid Screening of Tumor Immunomodulatory Drugs.

ACS nano
The tumor microenvironment plays a critical role in tumor progression and immune response, with the extracellular matrix (ECM) regulating immune cell infiltration. However, the interplay between ECM dynamics and tumor immunity remains poorly understo...