AIMC Topic: Zebrafish
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Machine learning enables high-throughput, low-replicate screening for novel anti-seizure targets and compounds using combined movement and calcium fluorescence in larval zebrafish.
European journal of pharmacology
Feb 4, 2025
Identifying new anti-seizure medications (ASMs) is difficult due to limitations in animal-based assays. Zebrafish (Danio rerio) serve as a model for chemical and genetic seizures, but current methods for detecting anti-seizure responses are limited b...
Marigold: a machine learning-based web app for zebrafish pose tracking.
BMC bioinformatics
Jan 28, 2025
BACKGROUND: High-throughput behavioral analysis is important for drug discovery, toxicological studies, and the modeling of neurological disorders such as autism and epilepsy. Zebrafish embryos and larvae are ideal for such applications because they ...
Development and applications of a machine learning model for an in-depth analysis of pentylenetetrazol-induced seizure-like behaviors in adult zebrafish.
Neuroscience
Jan 27, 2025
Epilepsy, a neurological disorder causing recurring seizures, is often studied in zebrafish by exposing animals to pentylenetetrazol (PTZ), which induces clonic- and tonic-like behaviors. While adult zebrafish seizure-like behaviors are well characte...
Using machine learning models to predict the dose-effect curve of municipal wastewater for zebrafish embryo toxicity.
Journal of hazardous materials
Jan 20, 2025
Municipal wastewater substantially contributes to aquatic ecological risks. Assessing the toxicity of municipal wastewater through dose-effect curves is challenging owing to the time-consuming, labor-intensive, and costly nature of biological assays....
The regulatory landscape of 5' UTRs in translational control during zebrafish embryogenesis.
Developmental cell
Jan 15, 2025
The 5' UTRs of mRNAs are critical for translation regulation during development, but their in vivo regulatory features are poorly characterized. Here, we report the regulatory landscape of 5' UTRs during early zebrafish embryogenesis using a massivel...
Anxiety in aquatics: Leveraging machine learning models to predict adult zebrafish behavior.
Neuroscience
Dec 13, 2024
Accurate analysis of anxiety behaviors in animal models is pivotal for advancing neuroscience research and drug discovery. This study compares the potential of DeepLabCut, ZebraLab, and machine learning models to analyze anxiety-related behaviors in ...
Deep learning-assisted detection of psychoactive water pollutants using behavioral profiling of zebrafish embryos.
Journal of hazardous materials
Oct 29, 2024
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
Sep 16, 2024
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
Sep 10, 2024
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
Sep 2, 2024
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...
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