KG-Predict: A knowledge graph computational framework for drug repurposing

Gao, Zhenxiang; Ding, Pingjian; Xu, Rong

doi:10.1016/j.jbi.2022.104133

Cited by 39 publications

(25 citation statements)

References 55 publications

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“…To identify relationships between these 28 proteins and AD, we performed advanced artificial intelligence-based computational analyses ( Figure 3 ). Using our KG-prediction approach [ 42 , 43 ], we ranked biomedical entities using our context-sensitive network-based algorithm. In total, 19,533 proteins were analyzed, and as shown ( Figure 3 D), the top ten predicted proteins from the 28 proteins that were significantly associated with AD ( p ≤ 0.01 vs. random expected ranking) were ranked.…”

Section: Resultsmentioning

confidence: 99%

Integrin β3-Mediated Cell Senescence Associates with Gut Inflammation and Intestinal Degeneration in Models of Alzheimer’s Disease

Tun

Wang

Gao

et al. 2023

IJMS

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Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by memory loss and personality changes that ultimately lead to dementia. Currently, 50 million people worldwide suffer from dementia related to AD, and the pathogenesis underlying AD pathology and cognitive decline is unknown. While AD is primarily a neurological disease of the brain, individuals with AD often experience intestinal disorders, and gut abnormalities have been implicated as a major risk factor in the development of AD and relevant dementia. However, the mechanisms that mediate gut injury and contribute to the vicious cycle between gut abnormalities and brain injury in AD remain unknown. In the present study, a bioinformatics analysis was performed on the proteomics data of variously aged AD mouse colon tissues. We found that levels of integrin β3 and β-galactosidase (β-gal), two markers of cellular senescence, increased with age in the colonic tissue of mice with AD. The advanced artificial intelligence (AI)-based prediction of AD risk also demonstrated the association between integrin β3 and β-gal and AD phenotypes. Moreover, we showed that elevated integrin β3 levels were accompanied by senescence phenotypes and immune cell accumulation in AD mouse colonic tissue. Further, integrin β3 genetic downregulation abolished upregulated senescence markers and inflammatory responses in colonic epithelial cells in conditions associated with AD. We provide a new understanding of the molecular actions underpinning inflammatory responses during AD and suggest integrin β3 may function as novel target mediating gut abnormalities in this disease.

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Section: Resultsmentioning

confidence: 99%

Integrin β3-Mediated Cell Senescence Associates with Gut Inflammation and Intestinal Degeneration in Models of Alzheimer’s Disease

Tun

Wang

Gao

et al. 2023

IJMS

Self Cite

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“…The ReproTox-KG is an initial effort towards integrating knowledge about birth defects, genes, and drugs. Similar efforts have been recently published, including studies that attempted to use graph embedding algorithms to predict missing/novel associations between drugs and diseases [67], for drug repurposing opportunities [68] [69], predicting drug targets [70] [71], adverse events [72], and drug-drug interactions [73]. These are just a few studies in this domain.…”

Section: Discussionmentioning

confidence: 99%

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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“…They used a relation extraction tool to extract semantic predications from PubMed abstract texts. Gao et al [16] constructed a knowledge graph based on associations and presented a computational approach to drug repurposing through lower-dimensional representation of entities and relations in the knowledge graph; they demonstrated the method for the case of Alzheimer's disease. Schartz et al [17] proposed a new fact-checking mechanism to explaining drug discovery hypotheses using knowledge graph patterns; while interesting, this was not a computational work.…”

Section: Related Workmentioning

confidence: 99%

Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case

Hamed¹,

Jończyk²,

Alam³

et al. 2022

Preprint

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This paper presents a computational approach designed to construct and query a literature-based knowledge graph for predicting novel drug therapeutics. The main objective is to offer a platform that discovers drug combinations from FDA-approved drugs and accelerates their investigations by domain scientists. Specifically, the paper introduced the following algorithms: (1) an algorithm for constructing the knowledge graph from drug, gene, and disease mentions in the biomedical literature; (2) an algorithm for vetting the knowledge graph from drug combinations that may pose a risk of drug interaction; (3) and two querying algorithms for searching the knowledge graph by a single drug or a combination of drugs. The resulting knowledge graph had 844 drugs, 306 gene/protein features, and 19 disease mentions. The original number of drug combinations generated was 2,001. We queried the knowledge graph to eliminate noise generated from chemicals that are not drugs. This step resulted in 614 drug combinations. When vetting the knowledge graph to eliminate the potentially risky drug combinations, it resulted in predicting 200 combinations. Our domain expert manually eliminated extra 54 combinations which left only 146 combination candidates. Our three-layered knowledge graph, empowered by our algorithms, offered a tool that predicted drug combination therapeutics for scientists who can further investigate from the viewpoint of drug targets and side effects.

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KG-Predict: A knowledge graph computational framework for drug repurposing

Cited by 39 publications

References 55 publications

Integrin β3-Mediated Cell Senescence Associates with Gut Inflammation and Intestinal Degeneration in Models of Alzheimer’s Disease

Integrin β3-Mediated Cell Senescence Associates with Gut Inflammation and Intestinal Degeneration in Models of Alzheimer’s Disease

ReproTox-KG: Toxicology Knowledge Graph for Structural Birth Defects

Mining Literature-Based Knowledge Graph for Predicting Combination Therapeutics: A COVID-19 Use Case

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