Representing Normal and Abnormal Physiology as Routes of Flow in ApiNATOMY

Bono, Bernard de; Gillespie, Tom; Surles-Zeigler, Monique C.; Kokash, Natallia; Grethe, Jeff S.; Martone, Maryann E.

doi:10.3389/fphys.2022.795303

Cited by 6 publications

(6 citation statements)

References 32 publications

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“…In addition, for each gene included in the KG, we computed likelihood for deleterious mutations using three established methods: (pLI) scores [39], RVIS [41], and Dosage sensitivity scores [20]. Each entity in the ReproTox KG also includes links out to databases based on entity ID resolution.…”

Section: Resultsmentioning

confidence: 99%

Section: Overall Construction and Composition Of The Reprotox Kgmentioning

confidence: 99%

“…To enhance the consistent representation of the above phenotypic terms, and to link these birth defects with knowledge about the appropriate anatomical entities involved with these pathologies, we manually curated the HPO terms onto an anatomy connectivity knowledge graph. The schema adopted by this graph is based on the ApiNATOMY knowledge representation [20,21], which was developed as part of the SPARC [22] connectivity mapping effort. The ApiNATOMY subgraph within the ReproTox KG provides links to knowledge about constituent anatomical structures such as cell types that may be involved in the birth defect mechanisms, as well as representations of abnormal anatomical organizations that typify these pathological phenotypes.…”

Section: Curating Phenotypic Terms Relevant To Birth Defectsmentioning

confidence: 99%

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ReproTox-KG: Toxicology Knowledge Graph for Structural Birth Defects

Evangelista

Clarke

Xie

et al. 2022

Preprint

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Birth defects are functional and structural abnormalities that impact 1 in 33 births in the United States. Birth defects have been attributed to genetic as well as other factors, but for most birth defects there are no known causes. Small molecule drugs, cosmetics, foods, and environmental pollutants may cause birth defects when the mother is exposed to them during pregnancy. These molecules may interfere with the process of normal fetal development. To 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 an initial focus on associations between birth defects, drugs, and genes. Specifically, to construct ReproTox-KG 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 data, known drug targets, genetic burden scores for all human genes, and placental crossing scores for all small molecules in ReproTox-KG. Using the data stored within ReproTox-KG, we scored 30,000 preclinical small molecules for their potential to induce birth defects. Querying the ReproTox-KG, we identified over 500 birth-defect/gene/drug cliques that can be used to explain molecular mechanisms for drug-induced birth defects. The ReproTox-KG is provided as curated tables and via a web-based user interface that can enable users to explore the associations between birth defects, approved and preclinical drugs, and human genes.

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Section: Resultsmentioning

confidence: 99%

Section: Overall Construction and Composition Of The Reprotox Kgmentioning

confidence: 99%

Section: Curating Phenotypic Terms Relevant To Birth Defectsmentioning

confidence: 99%

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ReproTox-KG: Toxicology Knowledge Graph for Structural Birth Defects

Evangelista

Clarke

Xie

et al. 2022

Preprint

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“…Knowledge graph models are mostly used for large, complex heterogeneous graphs, often containing hundreds or thousands of types of nodes and edges [73]. Because various heterogeneous graphs are so diferent from each other, researchers have developed a variety of dedicated knowledge graph models, so that each model has its own advantages [74].…”

Section: Discussionmentioning

confidence: 99%

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

Dan

et al. 2022

Computational Intelligence and Neuroscience

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MicroRNAs (miRNAs) are important types of noncoding RNAs, and there is a lack of holistic and systematic understanding of the functions they play in disease. We proposed a research strategy, including two parts network analysis and network modelling, to analyze, model, and predict the regulatory network of miRNAs from a network perspective, using unstable angina pectoris as an example. In the network analysis section, we proposed the WGCNA & SimCluster method using both correlation and similarity to find hub miRNAs, and validation on two datasets showed better results than the methods using correlation or similarity alone. In the network modelling section, we used six knowledge graph or graph neural network models for link prediction of three types of edges and multilabel classification of two types of nodes. Comparative experiments showed that the RotatE model was a good model for link prediction, while the RGCN model was the best model for multilabel classification. Potential target genes were predicted for hub miRNAs and validation of hub miRNA-target gene interactions, target genes as biomarkers and target gene functions were performed using a three-step validation approach. In conclusion, our study provides a new strategy to analyze and model miRNA regulatory networks.

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“…To enhance the consistent representation of the above phenotypic terms, and to link these birth defects with knowledge about the appropriate anatomical entities involved with these pathologies, we manually curated the HPO terms onto an anatomy connectivity knowledge graph. The schema adopted by this graph is based on the ApiNATOMY knowledge representation 30 , 31 , which was developed as part of the Stimulating Peripheral Activity to Relieve Conditions (SPARC) 32 connectivity mapping effort. The ApiNATOMY subgraph within the ReproTox-KG provides links to knowledge about constituent anatomical structures such as cell types that may be involved in the birth-defect mechanisms, as well as representations of abnormal anatomical organizations that typify these pathological phenotypes.…”

Section: Methodsmentioning

confidence: 99%

Toxicology knowledge graph for structural birth defects

et al. 2023

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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.

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Representing Normal and Abnormal Physiology as Routes of Flow in ApiNATOMY

Cited by 6 publications

References 32 publications

ReproTox-KG: Toxicology Knowledge Graph for Structural Birth Defects

ReproTox-KG: Toxicology Knowledge Graph for Structural Birth Defects

A Complex Heterogeneous Network Model of Disease Regulated by Noncoding RNAs: A Case Study of Unstable Angina Pectoris

Toxicology knowledge graph for structural birth defects

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