AIMC Topic: Reproduction

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Prediction of reproductive and developmental toxicity using an attention and gate augmented graph convolutional network.

Scientific reports May 25, 2025
Due to the diverse molecular structures of chemical compounds and their intricate biological pathways of toxicity, predicting their reproductive and developmental toxicity remains a challenge. Traditional Quantitative Structure-Activity Relationship ...
Humans Neural Networks, Computer Deep Learning Reproduction Quantitative Structure-Activity Relationship Computer Simulation Animals
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Fidelity to territory and mate and the causes and consequences of breeding dispersal in American goshawk (Astur atricapillus).

PloS one May 22, 2025
Using mark-resight data, we investigated fidelity to territory and mate as well as breeding dispersal rates and the causes and consequences of breeding dispersal in a 20-year study of American goshawks (Astur atricapillus) in Arizona, USA. Generalize...
Territoriality Animal Distribution Nesting Behavior Male Arizona Breeding Female Sexual Behavior, Animal Animals Reproduction
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Reproductive performance of Channa striata in wetland ecosystems: a fuzzy logic approach to water quality and eco-climatic factors for long-term sustainable management and aquaculture advancement.

Environmental science and pollution research international Dec 10, 2024
The striped snakehead, Channa striata, is commercially and nutritionally important due to its medicinal properties, such as wound healing and antimicrobial abilities. This study investigated the reproductive biology of C. striata in relation to hydro...
Animals Aquaculture Fishes Reproduction Ecosystem Fuzzy Logic Wetlands Water Quality Bangladesh
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Artificial Intelligence in Human Reproduction.

Archives of medical research Nov 29, 2024
The use of artificial intelligence (AI) in human reproduction is a rapidly evolving field with both exciting possibilities and ethical considerations. This technology has the potential to improve success rates and reduce the emotional and financial b...
Reproductive Techniques, Assisted Infertility Reproductive Medicine Pregnancy Complications Humans Artificial Intelligence Pregnancy Female Reproduction
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IMPACT OF REAL-LIFE ENVIRONMENTAL EXPOSURES ON REPRODUCTION: A contemporary review of machine learning to predict adverse pregnancy outcomes from pharmaceuticals, including DDIs.

Reproduction (Cambridge, England) Nov 15, 2024
IN BRIEF: Clinical drug trials often do not include pregnant people due to health risks; therefore, many medications have an unknown effect on the developing fetus. Machine learning QSAR models have been used successfully to predict the fetal risk of...
Reproduction Quantitative Structure-Activity Relationship Drug Interactions Drug-Related Side Effects and Adverse Reactions Machine Learning Pregnancy Pregnancy Outcome Environmental Exposure Female Humans
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Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations.

Poultry science Oct 29, 2024
Egg production rate and egg weight are core indicators for evaluating the production performance of broiler breeders. The accurate prediction of these indicators can significantly enhance farm economic efficiency and can provide a basis for future pr...
Machine Learning Animals Animal Husbandry Female Models, Biological Chickens Reproduction Oviposition Ovum
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FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Archives of toxicology Sep 18, 2024
Reproductive toxicity is one of the important issues in chemical safety. Traditional laboratory testing methods are costly and time-consuming with raised ethical issues. Only a few in silico models have been reported to predict human reproductive tox...
Humans Neural Networks, Computer Computer Simulation Toxicity Tests Reproduction Quantitative Structure-Activity Relationship
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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...
Endocrine Disruptors Zebrafish Reproduction Receptors, Androgen Animals Water Pollutants, Chemical Machine Learning Female
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Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models.

International journal of molecular sciences Jul 22, 2024
Reproductive toxicity poses significant risks to fertility and progeny health, making its identification in pharmaceutical compounds crucial. In this study, we conducted a comprehensive in silico investigation of reproductive toxic molecules, identif...
Reproduction Phthalic Acids Humans Computational Biology Support Vector Machine Computer Simulation Cluster Analysis Animals
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Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Environmental science & technology May 2, 2024
Chemical points of departure (PODs) for critical health effects are crucial for evaluating and managing human health risks and impacts from exposure. However, PODs are unavailable for most chemicals in commerce due to a lack of toxicity data. We the...
Risk Assessment Humans Reproduction Machine Learning
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