AIMC Topic: Teratogens
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Predictive biomarkers for embryotoxicity: a machine learning approach to mitigating multicollinearity in RNA-Seq.
Archives of toxicology
Sep 6, 2024
Multicollinearity, characterized by significant co-expression patterns among genes, often occurs in high-throughput expression data, potentially impacting the predictive model's reliability. This study examined multicollinearity among closely related...
Use of Natural Language Processing to Identify Sexual and Reproductive Health Information in Clinical Text.
Methods of information in medicine
Dec 20, 2023
OBJECTIVES: This study aimed to enable clinical researchers without expertise in natural language processing (NLP) to extract and analyze information about sexual and reproductive health (SRH), or other sensitive health topics, from large sets of cli...
Machine learning on drug-specific data to predict small molecule teratogenicity.
Reproductive toxicology (Elmsford, N.Y.)
May 16, 2020
Pregnant women are an especially vulnerable population, given the sensitivity of a developing fetus to chemical exposures. However, prescribing behavior for the gravid patient is guided on limited human data and conflicting cases of adverse outcomes ...
A new cyclic lipopeptide isolated from Bacillus amyloliquefaciens HAB-2 and safety evaluation.
Pesticide biochemistry and physiology
Aug 31, 2017
Bacillus is the most widely studied biocontrol agent and has been extensively used in the development of biopesticides and fungicides. In this study, a new cyclic lipopeptide was isolated from Bacillus amyloliquefaciens HAB-2 by column chromatography...
Machine Learning to Predict Teratogenicity: Theory and Practice.
Methods in molecular biology (Clifton, N.J.)
Jan 1, 2024
Machine learning (ML) is a subfield of artificial intelligence (AI) that consists of developing algorithms that can automatically learn patterns and relationships from data, without being explicitly programmed. It continues to advance with the develo...
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