AIMC Topic: Zebrafish

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Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.

Journal of hazardous materials
Water pollution poses a significant risk to the environment and human health, necessitating the development of innovative detection methods. In this study, a series of representative psychoactive compounds were selected as model pollutants, and a new...

Deep learning dives: Predicting anxiety in zebrafish through novel tank assay analysis.

Physiology & behavior
Behavior is fundamental to neuroscience research, providing insights into the mechanisms underlying thoughts, actions and responses. Various model organisms, including mice, flies, and fish, are employed to understand these mechanisms. Zebrafish, in ...

Leveraging new approach methodologies: ecotoxicological modelling of endocrine disrupting chemicals to Danio rerio through machine learning and toxicity studies.

Toxicology mechanisms and methods
New approach methodologies (NAMs) offer information tailored to the intended application while reducing the use of animals. NAMs aim to develop quantitative structure-activity relationship (QSAR) and quantitive-Read-Across structure-activity relation...

Knowledge-based machine learning for predicting and understanding the androgen receptor (AR)-mediated reproductive toxicity in zebrafish.

Environment international
Traditional methods for identifying endocrine-disrupting chemicals (EDCs) that activate androgen receptors (AR) are costly, time-consuming, and low-throughput. This study developed a knowledge-based deep neural network model (AR-DNN) to predict AR-me...

A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish.

Journal of neuroscience methods
BACKGROUND: Although zebrafish are increasingly utilized in biomedicine for CNS disease modelling and drug discovery, this generates big data necessitating objective, precise and reproducible analyses. The artificial intelligence (AI) applications ha...

A personalized mRNA signature for predicting hypertrophic cardiomyopathy applying machine learning methods.

Scientific reports
Hypertrophic cardiomyopathy (HCM) may lead to cardiac dysfunction and sudden death. This study was designed to develop a HCM signature applying bioinformatics and machine learning methods. Data of HCM and normal tissues were obtained from public data...

Application of machine learning in the study of development, behavior, nerve, and genotoxicity of zebrafish.

Environmental pollution (Barking, Essex : 1987)
Machine learning (ML) as a novel model-based approach has been used in studying aquatic toxicology in the environmental field. Zebrafish, as an ideal model organism in aquatic toxicology research, has been widely used to study the toxic effects of va...

Prediction of developmental toxic effects of fine particulate matter (PM) water-soluble components via machine learning through observation of PM from diverse urban areas.

The Science of the total environment
The global health implications of fine particulate matter (PM) underscore the imperative need for research into its toxicity and chemical composition. In this study, zebrafish embryos exposed to the water-soluble components of PM from two cities (Har...

Screening structure and predicting toxicity of pesticide adjuvants using molecular dynamics simulation and machine learning for minimizing environmental impacts.

The Science of the total environment
Surfactants as synergistic agents are necessary to improve the stability and utilization of pesticides, while their use is often accompanied by unexpected release into the environment. However, there are no efficient strategies available for screenin...

Bringing Artificial Intelligence (AI) into Environmental Toxicology Studies: A Perspective of AI-Enabled Zebrafish High-Throughput Screening.

Environmental science & technology
The booming development of artificial intelligence (AI) has brought excitement to many research fields that could benefit from its big data analysis capability for causative relationship establishment and knowledge generation. In toxicology studies u...