AIMC Topic: Machine Learning

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Integrating machine learning and nano-QSAR models to predict the oxidative stress potential caused by single and mixed carbon nanomaterials in algal cells.

Environmental toxicology and chemistry
In silico methods are increasingly important in predicting the ecotoxicity of engineered nanomaterials (ENMs), encompassing both individual and mixture toxicity predictions. It is widely recognized that ENMs trigger oxidative stress effects by genera...

Deciphering Explicit and Implicit Features for Reliable, Interpretable, and Actionable User Churn Prediction in Online Video Games.

IEEE transactions on visualization and computer graphics
The burgeoning online video game industry has sparked intense competition among providers to both expand their user base and retain existing players, particularly within social interaction genres. To anticipate player churn, there is an increasing re...

KMTLabeler: An Interactive Knowledge-Assisted Labeling Tool for Medical Text Classification.

IEEE transactions on visualization and computer graphics
The process of labeling medical text plays a crucial role in medical research. Nonetheless, creating accurately labeled medical texts of high quality is often a time-consuming task that requires specialized domain knowledge. Traditional methods for g...

A Multi-Model Ensemble for Advanced Prediction of Reverse Osmosis Performance in Full-Scale Zero-Liquid Discharge Systems.

Environmental science & technology
The growing reliance on reverse osmosis (RO) in zero liquid discharge (ZLD) and seawater desalination has underscored membrane fouling as a critical challenge, requiring predictive tools for proactive management. This study proposes a novel multidime...

Machine learning in biosignal analysis from wearable devices.

Materials horizons
The advancement of wearable bioelectronics has significantly improved real-time biosignal monitoring, enabling continuous health tracking and providing personalized medical insights. However, the sheer volume and complexity of biosignal data collecte...

Exploring the Reaction Network of Acetic Acid in Supercritical Water via Machine Learning Interatomic Potential.

Journal of chemical information and modeling
Supercritical water oxidation offers promising solutions for waste treatment, but understanding its complex molecular reaction mechanisms remains challenging due to extreme experimental conditions. We compare two computational approaches, a machine l...

Toxicity of nanoplastics: machine learning combined with meta-analysis.

Nanoscale horizons
Nanoplastics (NPs) are widespread in ecosystems, and their biohazards are of increasing concern. The hazards posed by NPs to aquatic and terrestrial plants as well as to aquatic animals have been extensively studied; however, their impact on mammals ...

Shape matters: inferring the motility of confluent cells from static images.

Soft matter
Cell motility in dense cell collectives is pivotal in various diseases like cancer metastasis and asthma. A central aspect in these phenomena is the heterogeneity in cell motility, but identifying the motility of individual cells is challenging. Prev...

Machine Learning Model for Predicting Sertraline-like Activities and Its Impact on Cancer Chemosensitization.

ACS chemical neuroscience
Selective serotonin reuptake inhibitors (SSRIs) like sertraline are crucial in treating depression and anxiety disorders, and studies indicate their potential as chemosensitizers in cancer therapy. This research develops a machine-learning predictive...

Hub biomarkers and their clinical relevance in glycometabolic disorders: A comprehensive bioinformatics and machine learning approach.

Chinese medical journal
BACKGROUND: Gluconeogenesis is a critical metabolic pathway for maintaining glucose homeostasis, and its dysregulation can lead to glycometabolic disorders. This study aimed to identify hub biomarkers of these disorders to provide a theoretical found...