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120 results for “Drug Repurposing”
The OREGANO knowledge graph for computational drug repurposing
<p>The files here are data files from the OREGANO project, which consists of building a holistic knowledge graph on drugs, including natural compounds. Here is the list of files:</p><p> </p><p>- OREGANO_V2.tsv : The triplet file used for link prediction. 3 columns : Subjet ; Predicate ; Object</p><p>- oreganov2.1_metadata_complet.ttl : The OREGANO knowledge graph in turtle format with the names and cross-references of the various integrated entities.</p><p> </p><p>The following files contain the cross-references of OREGANO entities according to their type. They are all organised as follows: the external sources are the titles of the columns and each line begins with the identifier of the entity in OREGANO :</p><p>- TARGET.tsv: Cross-reference table of the 22,096 targets.<br>- PHENOTYPES.tsv: Cross-reference table of the 11,605 phenotypes.<br>- DISEASES.tsv: Cross-reference table of the 18,333 diseases.<br>- PATHWAYS.tsv: Cross-reference table of the 2,129 pathways.<br>- GENES.tsv: Cross-reference table of the 35,794 genes.<br>- COMPOUND.tsv: Cross-reference table of the 90,868 compounds.<br>- INDICATIONS.tsv: Cross-reference table of the 2,714 indications.<br>- SIDE_EFFECT.tsv: Cross-reference table of the 6,060 side-effects.<br>- ACTIVITY.tsv: Names of the 78 activities.<br>- EFFECT.tsv: Names of the 171 effects.</p><p>The OREGANO knowledge graph is composed of 11 types of nodes and 19 types of links. The current version of the graph contains 88,937 nodes and 824,231 links.</p><p>A SPARQL endpoint has been provided to enable users to retrieve and explore the knowledge graph at <a href="http://91.121.148.199:8889/bigdata/#query">OREGANO SPARQL endpoint</a> .</p><p> </p><p>The integration files and the knowledge graph are available on the GitHub of the OREGANO project in the Integration folder: <a href="https://gitub.u-bordeaux.fr/erias/oregano">Gitub repository</a> .</p>
The OREGANO knowledge graph for computational drug repurposing
<p>The files here are data files from the OREGANO project, which consists of building a holistic knowledge graph on drugs, including natural compounds. Here is the list of files:</p><p> </p><p>- OREGANO_V2.tsv : The triplet file used for link prediction. 3 columns : Subjet ; Predicate ; Object</p><p>- oreganov2.1_metadata_complet.ttl : The OREGANO knowledge graph in turtle format with the names and cross-references of the various integrated entities.</p><p> </p><p>The following files contain the cross-references of OREGANO entities according to their type. They are all organised as follows: the external sources are the titles of the columns and each line begins with the identifier of the entity in OREGANO :</p><p>- TARGET.tsv: Cross-reference table of the 22,096 targets.<br>- PHENOTYPES.tsv: Cross-reference table of the 11,605 phenotypes.<br>- DISEASES.tsv: Cross-reference table of the 18,333 diseases.<br>- PATHWAYS.tsv: Cross-reference table of the 2,129 pathways.<br>- GENES.tsv: Cross-reference table of the 35,794 genes.<br>- COMPOUND.tsv: Cross-reference table of the 90,868 compounds.<br>- INDICATIONS.tsv: Cross-reference table of the 2,714 indications.<br>- SIDE_EFFECT.tsv: Cross-reference table of the 6,060 side-effects.<br>- ACTIVITY.tsv: Names of the 78 activities.<br>- EFFECT.tsv: Names of the 171 effects.</p><p>The OREGANO knowledge graph is composed of 11 types of nodes and 19 types of links. The current version of the graph contains 88,937 nodes and 824,231 links.</p><p>A SPARQL endpoint has been provided to enable users to retrieve and explore the knowledge graph at <a href="http://91.121.148.199:8889/bigdata/#query">OREGANO SPARQL endpoint</a> .</p><p> </p><p>The integration files and the knowledge graph are available on the GitHub of the OREGANO project in the Integration folder: <a href="https://gitub.u-bordeaux.fr/erias/oregano">Gitub repository</a> .</p><p> </p>
Prediction of repurposed drugs for treating lung injury in COVID-19
<p>These are output files of shared R scripts used in prediction of repurposed drugs for treating lung injury in COVID-19.</p> <p> </p> <p>R scripts are available here: https://doi.org/10.5281/zenodo.3822923</p> <p> </p> <p>Description of files:</p> <p>HCC515_6_data_for_drug.csv #Differential expression of genes in HCC515 cell at 6 h after treatment of ACE2 inhibitor</p> <p>HCC515_24_data_for_drug.csv #Differential expression of genes in HCC515 cell at 24 h after treatment of ACE2 inhibitor</p> <p>COVID19-Lung_data_for_drug.csv #Differential expression of genes in lung tissues with COVID-19</p> <p>HCC515_6_drug.csv #Drugs for HCC515 cell at 6 h after transfection of ACE2 inhibitor</p> <p>HCC515_24_drug.csv #Drugs for HCC515 cell at 24 h after transfection of ACE2 inhibitor</p> <p>COVID19-Lung_drug.csv #Drugs for lung tissuse from COVID-19 patients</p> <p>COL-3_single_treatment_response_data.csv #Differential expression of genes in HCC515 cell at 24h after treatment of COL-3</p> <p>CGP-60474_single_treatment_response_data.csv #Differential expression of genes in HCC515 cell at 24h after treatment of CGP-60474</p>
Dataset variants used in "Task-Driven Knowledge Graph Filtering Improves Prioritizing Drugs for Repurposing"
<p>This file contains all datasets and variants thereof used in the linked paper. We do not take credit for constructing the datasets, which has been done by the respective original authors (<a href="https://github.com/hetio/hetionet">https://github.com/hetio/hetionet</a>, <a href="https://github.com/gnn4dr/DRKG">https://github.com/gnn4dr/DRKG</a>). For our work we produced modified versions (called "subset" in the file) by applying our metapath based filtering approach. For validation purposed we also constructed ablation versions where one specific type of entities is missing (i.e. "nogene", "noside", etc).</p>
The repurposing of tebipenem pivoxil as alternative therapy for severe gastrointestinal infections caused by extensively drug resistant Shigella spp.
<p><strong>dataset for The repurposing of tebipenem pivoxil as alternative therapy for severe gastrointestinal infections caused by extensively drug resistant <em>Shigella</em> spp.</strong></p> <p>Elena Fernández Alvaro <sup>1*</sup>, Phat Voong Vinh <sup>2</sup>, Cristina de Cozar <sup>1</sup>, David Wille <sup>1</sup>, Beatriz Urones <sup>1</sup>,</p> <p>Alan Price <sup>1</sup>, Nhu Tran Do Hoang <sup>2</sup>, Tuyen Ha Thanh <sup>2</sup>, Molly McCloskey <sup>3</sup>, Shareef Shaheen<sup> 3</sup>, Denise Dayao<sup> 4</sup>, Jaime de Mercado <sup>1</sup>, Pablo Castañeda <sup>1</sup>, Adolfo García-Perez <sup>1</sup>, Benson Singa <sup>5</sup>, Patricia Pavlinac <sup>6</sup>,</p> <p>Judd Walson<sup>3</sup>, Maria Santos Martínez-Martínez <sup>1</sup>, Samuel L.M. Arnold <sup>3</sup>, Tzipori Saul <sup>4</sup>, Lluis Ballell <sup>1#</sup>,</p> <p>and Stephen Baker <sup>7,8*</sup></p> <p> </p>
TRANSCRIPT drug repurposing dataset
<p>Version 2.0.0 (05/29/2023)</p> <p>This is a drug repurposing dataset under MIT licence, compiled by Dr. Clémence Réda <clemence.reda@uni-rostock.de> at Universität Rostock, comprising a drug-disease association matrix, and several drug-drug and disease-disease similarity matrices. It only uses transcriptomic data (i.e., gene activity/expression). The sparsity number is the percentage of nonzero values in the association matrix.</p> <p># drugs | # diseases | Sparsity number | # positive associations | # negative associations | # genes<br> ------- | ---------- | --------------- | ----------------------- | ----------------------- | -------<br> 204 | 116 | 0.44% | 401 | 11 | 12,096</p> <p>All drugs (resp., diseases) are associated with a gene expression feature vector of length 12,096 (that is, all drugs and diseases in the feature matrices appear in the association matrix, and vice versa).</p> <p>----------</p> <p>This dataset consists of three .CSV files:</p> <p>* Drug-Disease Association Matrix</p> <p>1. "ratings_mat.csv"</p> <p>This matrix contains values in {-1,0,1} where -1 stands for a negative association (i.e., the drug failed for some reason to treat the considered disease: e.g., lack of accrual in the associated clinical trial, or proven toxicity), 1 for a positive association (i.e., the drug was shown to treat the disease), and 0 for unknown associated status. The columns are diseases, identified by their MedGen Concept ID, whereas rows are drugs, identified by their DrugBank IDs or PubChem CIDs.</p> <p>* Drug Feature Matrix</p> <p>1. "items.csv"</p> <p>This matrix has drugs in its columns, identified by their DrugBank IDs or PubChem CIDs, and genes in its rows, identified by their HUGO Gene Symbol. Genewise transcriptomic variation induced by drug treatment, from the CREEDS or the LINCS L1000 databases.</p> <p>* Disease Feature Matrix</p> <p>1. "users.csv"</p> <p>This matrix has diseases in its columns, identified by their MedGen Concept IDs, and genes in its rows, identified by their HUGO Gene Symbol. Genewise transcriptomic variation induced by the disease, from the CREEDS database.</p> <p>----------</p> <p>Further information about the generation of those matrices is available by running the Jupyter notebook TRANSCRIPT_dataset.ipynb on the following GitHub repository: https://github.com/RECeSS-EU-Project/drug-repurposing-datasets. For any questions, please contact the author at <clemence.reda@uni-rostock.de> or the RECeSS project contributors at <recess-project@proton.me>.</p>
PREDICT drug repurposing dataset
<p>/!\ LATEST VERSION IS LOCATED at the following page (v2.0.1): https://zenodo.org/record/7983090</p> <p>Version 1.0.3 (06/27/2023)</p> <p>----</p> <p>CHANGELOG:</p> <p>- Same as v1.0.0, except for the drug similarity matrices, which are no longer empty and ratings_mat.csv is present.</p> <p>---</p> <p>This is a drug repurposing dataset, compiled by Dr. Clémence Réda <clemence.reda@uni-rostock.de>, comprising a drug-disease association matrix, and several drug-drug and disease-disease similarity matrices. It uses the same type of data than the dataset compiled by Gottlieb et al., 2011 [PMID: 21654673]. The sparsity number is the percentage of nonzero values in the association matrix.</p> <p># drugs | # diseases | Sparsity number | # positive associations | # negative associations<br> ------- | ---------- | --------------- | ----------------------- | -----------------------<br> 1,395 | 1,501 | 0.38% | 8,240 | 295</p> <p>Note that not all drugs (resp., diseases) might have an associated feature vector (that is, all drugs and diseases in the similarity matrix appear in the association matrix, but not necessarily the other way around).</p> <p>----------</p> <p>This dataset consists of five .CSV files:</p> <p>* Drug-Disease Association Matrix</p> <p>1. "ratings_mat.csv"</p> <p>This matrix contains values in {-1,0,1} where -1 stands for a negative association (i.e., the drug failed for some reason to treat the considered disease: e.g., lack of accrual in the associated clinical trial, or proven toxicity), 1 for a positive association (i.e., the drug was shown to treat the disease), and 0 for unknown associated status. The columns are diseases, identified by their MedGen Concept ID, whereas rows are drugs, identified by their DrugBank IDs or PubChem CIDs.</p> <p>* Drug-Drug Similarity Matrix</p> <p>All drug-drug similarity matrices have drugs in their columns and rows, identified by their DrugBank IDs or PubChem CIDs.</p> <p>1. "se_PREDICT_matrix.csv"</p> <p>Jaccard score similarity between one-hot encodings of the side effects reported for drugs.</p> <p>2. "signature_PREDICT_matrix.csv"</p> <p>Jaccard score similarity between drug signatures (from the CREEDS or the LINCS L1000 databases), that is, vectors reporting the genewise change in activity due to treatment.</p> <p>Other types of similarity matrices and further information about the generation of those matrices are available by running the Jupyter notebook PREDICT_dataset_v1.0.0.ipynb on the following GitHub repository: https://github.com/RECeSS-EU-Project/drug-repurposing-datasets.</p> <p>* Disease-Disease Similarity Matrix</p> <p>All disease-disease similarity matrices have diseases in their columns and rows, identified by their MedGen Concept IDs.</p> <p>1. "disease_semantic_PREDICT_matrix.csv"</p> <p>Resnik semantic similarity between onthology nodes associated with diseases (from the HPO database).</p> <p>2. "disease_phenotype_PREDICT_matrix.csv"</p> <p>Jaccard score similarity between disease phenotypes (from the CREEDS database), that is, vectors reporting the genewise change in activity due to the disease.</p> <p>Other types of similarity matrices and further information about the generation of those matrices are available by running the Jupyter notebook PREDICT_dataset_v1.0.0.ipynb on the following GitHub repository: https://github.com/RECeSS-EU-Project/drug-repurposing-datasets.</p> <p>----------</p> <p>For any questions, please contact the author at <clemence.reda@uni-rostock.de> or the RECeSS project contributors at <recess-project@proton.me>.</p>
DTA Atlas: A Massive-Scale Drug Repurposing Database
<p>The database consists of affinity predictions on a wide selection of drugs versus all proteins in the human proteome from advanced deep neural networks.</p>
Differential Gene Expression Datasets for "Identification of candidate repurposable drugs to combat COVID‑19 using a signature‑based approach"
<p>This dataset has the unfiltered transcriptome differential expression results used in the paper "Identification of candidate repurposable drugs to combat COVID‑19 using a signature‑based approach". </p>
# Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease
<p>Dysregulation of gene expression in Alzheimer’s disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern target gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, it is still challenging to understand how cell type networks work abnormally under AD. To address this, we integrated single-cell multi-omics data and predicted the gene regulatory networks in AD and control for four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes. Importantly, we applied network biology approaches to analyze the changes of network characteristics across these cell types, and between AD and control. For instance, many hub TFs target different genes between AD and control (rewiring). Also, these networks show strong hierarchical structures in which top TFs (master regulators) are largely common across cell types, whereas different TFs operate at the middle levels in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes and putative targets of approved and pending AD drugs, suggesting possible cell-type genomic medicine in AD. Finally, using the cell type gene regulatory networks, we developed machine learning models to classify and prioritize additional AD genes. We found that top prioritized genes predict clinical phenotypes (e.g., cognitive impairment) with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals dysregulated cell type gene dysregulatory mechanisms in AD.</p>
Porte drug repurposing dataset for epilepsy
<p>Version 1.0.0 (05/26/2023)</p> <p>This is a drug repurposing dataset on epilepsy, compiled by Dr. Baptiste PORTE <baptiste.porte@inserm.fr> in January 2021 at Inserm UMR 1141 Neurodiderot, 48 boulevard Sérurier, F-75019 Paris, France. Its author has authorized the publication under the MIT licence of this dataset by Clémence Réda from the RECeSS project (Grant ID 101102016) on May 20th, 2023.</p> <p>It consists in two .CSV files:</p> <p>1. Liste anticonvu.csv with 6 columns</p> <p>"Compound CID": Best match PubChem CID for the considered chemical compound<br> "drug_name": The common drug name<br> "score": preliminary drug class in {0: unknown effect on epileptic patients, 1: antiepileptic drug, that is, treatment for epileptic patients}<br> "verification": 1st bibliographic round for drug class in {0: unknown effect on epileptic patients, 1: antiepileptic drug, that is, treatment for epileptic patients}<br> "verif 2": final assigned drug class in {0: unknown effect on epileptic patients, 1: antiepileptic drug, that is, treatment for epileptic patients}<br> "details": justification -in French- for the final assigned drug class</p> <p>2. Liste proconvu.csv</p> <p>"Compound CID": Best match PubChem CID for the considered chemical compound<br> "drug_name": The common drug name<br> "score": preliminary drug class in {0: unknown effect on epileptic patients, -1: proconvulsant drug, that is, seizure-inducing}<br> "verification": 1st bibliographic round for drug class in {0: unknown effect on epileptic patients, -1: proconvulsant drug, that is, seizure-inducing}<br> "verif 2": final assigned drug class in {0: unknown effect on epileptic patients, -1: proconvulsant drug, that is, seizure-inducing}<br> "effet convulsivant demontré": justification -in French- for the final assigned drug class</p> <p>For any questions, please contact the author at <baptiste.porte@inserm.fr> or the RECeSS project contributors at <recess-project@proton.me>.</p>
Data for DRExM³L: Drug REpurposing using eXplainable Machine Learning and Mechanistic Models of signal transduction
<p>(DREM³L) Drug REpurposing using Mechanistic Models of signal transduction and Machine Learning </p>
Drug Repurposing Central Usage Statistics Matomo
<p>Usage for the Open Science publishing portal Drug Repurposing Central created by ScienceOpen as part of the REPO4EU project. This dataset contains visits over time via Matomo Analytics. Current time frame is 01 September, 2023 to 31 August, 2024.</p>
Drug Repurposing Central Portal Usage and Geolocation Statistics
<p>Record-level usage and geolocation statistics by country and organization for the Drug Repurposing Central portal.</p> <p>Covered time frame is from October 2021 to August 2024.</p>
Supplementary data for "The mechanistic functional landscape of Retinitis Pigmentosa: an ML-driven approach to drug repurposing"
<p>Supplementary data for "The mechanistic functional landscape of Retinitis Pigmentosa: an ML-driven approach to drug repurposing"</p><p> </p><p>version: 10.5281/zenodo.10203479</p><ul><li>added missing file: "drug_actions_withSimplAction.csv"</li></ul>
Towards a more inductive world for drug repurposing approaches
<pre>This is the official Zenodo repository of the paper "Towards a more inductive world for drug repurposing approaches". The related arXiv publication can be found <a href="https://arxiv.org/abs/2311.12670" target="_blank" rel="noopener"><em>here</em></a>. <br><br>Please refer to the README.md file for comprehensive information about the uploaded files.<br><br>The GitHub Repository with the code can be accesed here: <a href="https://github.com/ML4BM-Lab/GraphEmb/" target="_blank" rel="noopener">https://github.com/ML4BM-Lab/GraphEmb/</a>.</pre>
Systematic creation and phenotyping of Mendelian disease models in C. elegans: towards large-scale drug repurposing
<p>Data collected for the eLife OpenAccess paper: Systematic creation and phenotyping of Mendelian disease models in <em>C. elegans</em>: towards large-scale drug repurposing. (doi: 10.7554/eLife.92491.1)</p> <p>Contains: extracted features, calculated stats, normalised z-scores and timerseries data of all the disease model mutants generated. In addition, there is a static .html file that allows for mousing over the clustermaps to easily view differences in strains compared to the N2 wild-type. Dataset also contains, metadata and feature summary/file name information of FDA-library drug screen and the confirmation screen of the hit from this (i.e., all data collected in published in the associated paper). </p>
SGLT2-Inhibition reverts urinary peptide changes associated with severe COVID-19: an in-silico proof-of-principle of proteomics-based drug repurposing
<p>Severe COVID-19 is reflected by significant changes in urine peptides. Based on this observation, a clinical test predicting COVID-19 severity, CoV50, was developed and registered as in vitro diagnostic in Germany. We have hypothesized that molecular changes displayed by CoV50, likely reflective of endothelial damage, may be reversed by specific drugs. Such an impact by a drug could indicate potential benefits in the context of COVID-19. To test this hypothesis, urinary peptide data from patients without COVID-19 prior to and after drug treatment were collected from the human urinary proteome database. The drugs chosen were selected based on availability of sufficient number of participants in the dataset (n>20) and potential value of drug therapies in the treatment of COVID-19 based on reports in the literature. In these participants without COVID-19, spironolactone did not demonstrate a significant impact on CoV50 scoring. Empagliflozin treatment resulted in a significant change in CoV50 scoring, indicative of a potential therapeutic benefit. The study serves as a proof-of-principle for a drug repurposing approach based on human urinary peptide signatures. The results support the initiation of a randomised control trial testing a potential positive effect of empagliflozin for severe COVID-19, possibly via endothelial protective mechanisms.</p>
Network-based Drug Repurposing for Human Coronavirus
<p>Datasets from a study in which network-based methodologies were used to identify potential repurposable drugs and drug combinations for successfully targeting the 2019 novel coronavirus.</p>
OpenPREDICT: Open and FAIR implementation of the PREDICT method for drug repurposing
<p>This dataset is used for Open and FAIR implementation of the PREDICT method. It is described in the paper titled "PREDICT: a method for inferring novel drug indications with application to personalized medicine.", Gottlieb A, Stein GY, Ruppin E, Sharan R. Mol Syst Biol. 2011;7:496. Published 2011 Jun 7. doi:10.1038/msb.2011.26</p>
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