A standard database for drug repositioning

Brown, Adam S.; Patel, Chirag

doi:10.1038/sdata.2017.29

Cited by 266 publications

(177 citation statements)

References 22 publications

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“…Although our performance scores reported are on held-out datasets, it is possible that patterns connecting well known relations may not be present in newly discovered relations. repoDB [5] is a database that has over 6000 FDA approved drugs and corresponding indications collected from the regularly updated DrugCentral platform [35]. As such repoDB is expected to contain the latest FDA approved drugs.…”

Section: Experiments and Resultsmentioning

confidence: 99%

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Bakal

Talari

Kakani

et al. 2018

Journal of Biomedical Informatics

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Background Identifying new potential treatment options for medical conditions that cause human disease burden is a central task of biomedical research. Since all candidate drugs cannot be tested with animal and clinical trials, in vitro approaches are first attempted to identify promising candidates. Likewise, identifying different causal relations between biomedical entities is also critical to understand biomedical processes. Generally, natural language processing (NLP) and machine learning are used to predict specific relations between any given pair of entities using the distant supervision approach. Objective To build high accuracy supervised predictive models to predict previously unknown treatment and causative relations between biomedical entities based only on semantic graph pattern features extracted from biomedical knowledge graphs. Methods We used 7,000 treats and 2,918 causes hand-curated relations from the UMLS Metathesaurus to train and test our models. Our graph pattern features are extracted from simple paths connecting biomedical entities in the SemMedDB graph (based on the well-known SemMedDB database made available by the U.S. National Library of Medicine). Using these graph patterns connecting biomedical entities as features of logistic regression and decision tree models, we computed mean performance measures (precision, recall, F-score) over 100 distinct 80%–20% train-test splits of the datasets. For all experiments, we used a positive:negative class imbalance of 1:10 in the test set to model relatively more realistic scenarios. Results Our models predict treats and causes relations with high F-scores of 99% and 90% respectively. Logistic regression model coefficients also help us identify highly discriminative patterns that have an intuitive interpretation. We are also able to predict some new plausible relations based on false positives that our models scored highly based on our collaborations with two physician co-authors. Finally, our decision tree models are able to retrieve over 50% of treatment relations from a recently created external dataset. Conclusions We employed semantic graph patterns connecting pairs of candidate entities in a knowledge graph as features to predict treatment/causative relations between them. We provide what we believe is the first evidence in direct prediction of biomedical relations based on graph features. Our work complements lexical pattern based approaches in that the graph patterns can be used as additional features for weakly supervised relation prediction.

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Section: Experiments and Resultsmentioning

confidence: 99%

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Bakal

Talari

Kakani

et al. 2018

Journal of Biomedical Informatics

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“…With new and powerful screening assays and prediction tools, in silico structure-based screening of these large chemical libraries is identifying potential inhibitors of viral infections including repurposed compounds that target a variety of viral proteins and host factors (Abu Bakar and Ng, 2018;Barrows et al, 2016;Dowall et al, 2016;Martinez et al, 2015;Yuan et al, 2017). Another strategy that will surely reveal more promising candidates is based on comparing data from databases of pathogens (Brown and Patel, 2017;Sharma et al, 2015) with databases of drugs, e.g., drug information from the National Library of Medicine (https://www.nlm.nih.gov/learn-about-drugs.html). Imaging studies combined with functional assays will show the localization and intracellular effects of these new inhibitors (Panchal et al, 2010).…”

Section: Discussionmentioning

confidence: 99%

Drug repurposing for new, efficient, broad spectrum antivirals

2019

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Emerging viruses are a major threat to human health. Recent outbreaks have emphasized the urgent need for new antiviral treatments. For several pathogenic viruses, considerable efforts have focused on vaccine development. However, during epidemics infected individuals need to be treated urgently. High-throughput screening of clinically tested compounds provides a rapid means to identify undiscovered, antiviral functions for wellcharacterized therapeutics. Repurposed drugs can bypass part of the early cost and time needed for validation and authorization. In this review we describe recent efforts to find broad spectrum antivirals through drug repurposing. We have chosen several candidates and propose strategies to understand their mechanism of action and to determine how resistance to antivirals develops in infected cells.

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“…Information contained in RepoDB [26] was exploited to obtain a list of new repurposed drugs for breast cancer. RepoDB includes a gold standard set of drug repositioning which have been failed or succeeded.…”

Section: New Repurposed Drugs For Breast Cancermentioning

confidence: 99%

RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

Fahimian¹,

Zahiri²,

Arab³

et al. 2019

Preprint

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Background: It often takes more than 10 years and costs more than one billion dollars to develop a new drug for a disease and bring it to the market. Drug repositioning can significantly reduce costs and times in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. Methods:In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results:The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. Final drug repositioning model has been built based on random forest classifier, after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion:Results show the strength of the proposed method in detecting true drug-disease relationships.RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab and Tamoxifen.

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A standard database for drug repositioning

Cited by 266 publications

References 22 publications

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Exploiting semantic patterns over biomedical knowledge graphs for predicting treatment and causative relations

Drug repurposing for new, efficient, broad spectrum antivirals

RepCOOL: Computational Drug Repositioning Via Integrating Heterogeneous Biological Networks

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