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Reproduction

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DTLR-CS: Deep tensor low rank channel cross fusion neural network for reproductive cell segmentation.

PloS one
In recent years, with the development of deep learning technology, deep neural networks have been widely used in the field of medical image segmentation. U-shaped Network(U-Net) is a segmentation network proposed for medical images based on full-conv...

An artificial intelligence-based model for predicting reproductive toxicity of bisphenol analogues mixtures to the rotifer Brachionus calyciflorus.

The Science of the total environment
The joint toxicity effects of mixtures, particularly reproductive toxicity, one of the main causes of aquatic ecosystem degradation, are often overlooked as it is impractical to test all mixtures. This study developed and evaluated the following mode...

Two-Stage Machine Learning-Based Approach to Predict Points of Departure for Human Noncancer and Developmental/Reproductive Effects.

Environmental science & technology
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...

Computational Insights into Reproductive Toxicity: Clustering, Mechanism Analysis, and Predictive Models.

International journal of molecular sciences
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...

FGTN: Fragment-based graph transformer network for predicting reproductive toxicity.

Archives of toxicology
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...

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...

Predicting egg production rate and egg weight of broiler breeders based on machine learning and Shapley additive explanations.

Poultry science
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...

Artificial Intelligence in Human Reproduction.

Archives of medical research
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...

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)
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...

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
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...