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Assessment and Evaluation of the High Risk Neonate: The NICU Network Neurobehavioral Scale
Published on: August 25, 2014
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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.
Communications Medicine
|July 17, 2023
Summary
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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