AIMC Topic: Machine Learning

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Data-driven machine learning modeling reveals the impact of micro/nanoplastics on microalgae and their key underlying mechanisms.

Journal of hazardous materials
Micro- and nano-plastics (MNPs) pose a growing threat to freshwater microalgae, leading to water quality and biodiversity. Traditional experiments often encounter difficulties in terms of cost, time, and capturing complex interactions when exploring ...

An automated machine learning-based framework for predicting groundwater quality with sensor data.

Journal of environmental management
Groundwater quality monitoring stands as a critical aspect of groundwater management, necessitating real-time and accurate measurement technologies. In this study, we introduce an automated framework for predicting NH-N in groundwater using multipara...

Predicting the Sorption Capacity of Perfluoroalkyl and Polyfluoroalkyl Substances in Soils: Meta-Analysis and Machine Learning Modeling.

Environmental science & technology
Predicting the soil sorption capacity for perfluoroalkyl and polyfluoroalkyl substances (PFAS) is pivotal for environmental risk assessment. However, traditional experimental methods are inefficient, necessitating computational model development. We ...

Augmenting MACCS Keys with Persistent Homology Fingerprints for Protein-Ligand Binding Classification.

Journal of chemical information and modeling
Machine learning has become an essential tool in computational drug design, enabling models to uncover patterns in molecular data and predict protein-ligand interactions. This study introduces a novel approach by integrating persistence images with M...

Data-Driven Sustainable Campaigns to Decipher Invasive Breast Cancer Features.

ACS biomaterials science & engineering
The intrinsic complexity of biological processes often hides the role of dynamic microenvironmental cues in the development of pathological states. Microphysiological systems (MPSs) are emerging technological platforms that model dynamics of tissue-...

Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.

Annals of medicine
BACKGROUND: High intraoperative blood pressure variability (HIBPV) is significantly associated with postoperative adverse complications. However, practical tools to characterize perioperative factors associated with HIBPV remain limited. This study a...

Machine learning approach to DNA methylation and neuroimaging signatures as biomarkers for psychological resilience in young adults.

Behavioural brain research
Psychological resilience is influenced by both psychological and biological factors. However, the potential of using DNA methylation (DNAm) probes and brain imaging variables to predict psychological resilience remains unclear. This study aimed to in...

Transfer learning enables robust prediction of cellular toxicity from environmental micro- and nanoplastics.

Journal of hazardous materials
Micro- and nanoplastics (MNPs) are emerging pollutants that accumulate in ecosystems, food chains, and the human body, raising concerns about human health risks. However, understanding their toxicity remains challenging due to limited experimental da...

Biomacromolecular engineering of redox-active chitosan-polypyrrole-clay hybrid materials for machine learning-assisted desulfurization and supercapacitor application.

International journal of biological macromolecules
The bentonite chitosan polypyrrole (Bent-CS-PPy) composite was engineered as a multifunctional material with dual capabilities: the efficient adsorption of dibenzothiophene (DBT) from model fuel and application as an electrode in electrochemical ener...

Integrating Non-Targeted Mass Spectrometry and Machine Learning for the Classification of Organic and Conventionally Grown Agricultural Products: A Case Study on Tomatoes.

Journal of agricultural and food chemistry
Rising demand for organic agricultural products has made the verification of their authenticity a critical concern. Traditional classification approaches for mass spectrometry using full-scan high-resolution mass spectrometry data often emphasize fea...