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Updated: Jul 23, 2025

Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
Toxicology knowledge graph for structural birth defects
John Erol Evangelista1, Daniel J B Clarke1, Zhuorui Xie1
1Department of Pharmacological Sciences, Mount Sinai Center for Bioinformatics, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
Background:
Birth defects are functional and structural abnormalities that impact about 1 in 33 births in the United States. They have been attributed to genetic and other factors such as drugs, cosmetics, food, and environmental pollutants during pregnancy, but for most birth defects there are no known causes.
Methods:
To further characterize associations between small molecule compounds and their potential to induce specific birth abnormalities, we gathered knowledge from multiple sources to construct a reproductive toxicity Knowledge Graph (ReproTox-KG) with a focus on associations between birth defects, drugs, and genes. Specifically, we gathered data from drug/birth-defect associations from co-mentions in published abstracts, gene/birth-defect associations from genetic studies, drug- and preclinical-compound-induced gene expression changes in cell lines, known drug targets, genetic burden scores for human genes, and placental crossing scores for small molecules.
Results:
Using ReproTox-KG and semi-supervised learning (SSL), we scored >30,000 preclinical small molecules for their potential to cross the placenta and induce birth defects, and identified >500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG can be accessed via a web-based user interface available at https://maayanlab.cloud/reprotox-kg . This site enables users to explore the associations between birth defects, approved and preclinical drugs, and all human genes.
Conclusions:
ReproTox-KG provides a resource for exploring knowledge about the molecular mechanisms of birth defects with the potential of predicting the likelihood of genes and preclinical small molecules to induce birth defects.
Insights
A new knowledge graph, ReproTox-KG, identifies potential teratogens by analyzing drug and gene associations with birth defects. This resource aids in predicting risks from preclinical small molecules and understanding molecular mechanisms.
Area of Science:
- Biomedical Informatics
- Toxicology
- Genetics
Background:
- Birth defects affect approximately 1 in 33 US births, with many having unknown causes.
- Potential contributing factors include genetic elements, environmental pollutants, and drug exposure during pregnancy.
Purpose of the Study:
- To develop a comprehensive knowledge graph (ReproTox-KG) to characterize associations between small molecules, genes, and birth defects.
- To identify molecular mechanisms underlying drug-induced birth defects.
Main Methods:
- Constructed ReproTox-KG by integrating data on drug/birth-defect co-mentions, gene/birth-defect associations, gene expression changes, drug targets, genetic burden scores, and placental crossing.
- Applied semi-supervised learning (SSL) to score preclinical small molecules for teratogenic potential.
Main Results:
- Scored over 30,000 preclinical small molecules for placental crossing and birth defect induction potential.
- Identified over 500 birth-defect/gene/drug cliques to elucidate drug-induced birth defect mechanisms.
- Launched a web-based interface for ReproTox-KG: https://maayanlab.cloud/reprotox-kg.
Conclusions:
- ReproTox-KG serves as a valuable resource for understanding birth defect molecular mechanisms.
- The platform can predict the teratogenic potential of genes and preclinical small molecules.
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