AIMC Topic: Zebrafish
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A novel open-access artificial-intelligence-driven platform for CNS drug discovery utilizing adult zebrafish.
Journal of neuroscience methods
Aug 24, 2024
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
Jul 24, 2024
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)
Jun 28, 2024
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
Jun 19, 2024
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
Jun 6, 2024
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
May 1, 2024
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...
Artificial Intelligence-Assisted Optimization of Antipigmentation Tyrosinase Inhibitors: Molecular Generation Based on a Low Activity Lead Compound.
Journal of medicinal chemistry
Apr 23, 2024
Artificial intelligence (AI) molecular generation is a highly promising strategy in the drug discovery, with deep reinforcement learning (RL) models emerging as powerful tools. This study introduces a fragment-by-fragment growth RL forward molecular...
Comparing robotic and manual injection methods in zebrafish embryos for high-throughput RNA silencing using CRISPR-RfxCas13d.
BioTechniques
Feb 29, 2024
In this study, the authors compared the efficiency of automated robotic and manual injection methods for the CRISPR-RfxCas13d (CasRx) system for mRNA knockdown and Cas9-mediated DNA targeting in zebrafish embryos. They targeted the no tail () gene as...
GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM.
BMC bioinformatics
Feb 27, 2024
BACKGROUND: Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the info...
MCPNET: Development of an interpretable deep learning model based on multiple conformations of the compound for predicting developmental toxicity.
Computers in biology and medicine
Feb 7, 2024
The development of deep learning models for predicting toxicological endpoints has shown great promise, but one of the challenges in the field is the accuracy and interpretability of these models. The bioactive conformation of a compound plays a crit...
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