The OREGANO knowledge graph for computational drug repurposing

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: - OREGANO_V2.tsv : The triplet file used for link prediction. 3 columns : Subjet ; Predicate ; Object - oregano_info_complet.ttl : The OREGANO knowledge graph in turtle format with the names and cross-references of the various integrated entities. 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 : - TARGET.tsv: Cross-reference table of the 21,987 targets. - PHENOTYPES.tsv: Cross-reference table of the 6,930 phenotypes. - DISEASES.tsv: Cross-reference table of the 8,862 diseases. - PATHWAYS.tsv: Cross-reference table of the 2,062 pathways. - GENES.tsv: Cross-reference table of the 19,938 genes. - COMPOUND.tsv: Cross-reference table of the 28,069 compounds. - INDICATIONS.tsv: Cross-reference table of the 2,080 indications. - SIDE_EFFECT.tsv: Cross-reference table of the 5,364 side-effects. - ACTIVITY.tsv: Names of the 76 activities. - EFFECT.tsv: Names of the 162 effects. The OREGANO knowledge graph is composed of 11 types of nodes and 19 types of links. The current version of the graph contains 99,651 nodes and 788,425 links. A SPARQL endpoint has been provided to enable users to retrieve and explore the knowledge graph at OREGANO SPARQL endpoint . The integration files and the knowledge graph are available on the GitHub of the OREGANO project in the Integration folder: Gitub repository .Funding : The PhD thesis of Marina Boudin is supported within the framework of the Digital Public Health programme of PIA3 (Investment for the future). Project reference: 17-EURE-0019

The OREGANO knowledge graph for computational drug repurposing

Abstract Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano )

Plateforme d'extraction et d'analyse de conversation

Dans le cadre d’une étude visant à développer un agent conversationnel empathique, une plateforme a été développée afin de collecter et analyser des conversations de personnes depuis plusieurs applications de messagerie couramment utilisées. Cet article donne un aperçu de la conception et du développement de cette plateforme web qui va être testée prochainement avec de vrais utilisateurs

A box full of surprises. Archaeologists find an ancient relic in St Rumbold’s Cathedral (Mechelen)

Conference website http://org.kikirpa.be/relicsatthelab/index.phpstatus: publishe