This page presents my former research projects<\/p>\n
PullCoVapart – Pull the Covid-19 replicative catalytic core apart<\/strong><\/em><\/p>\n This is an interdisciplinary project (with two laboratories in Marseille; AFMB and LIS, funded by ANR and INSERM), combining methods in artificial intelligence with protein biochemistry to propose therapeutic options to Covid-19. The three main objectives of the project are to (1) reconstitute, in vitro, the replication complex of the COVID-19; (2) model and simulate its replication activity in silico and finally (3) compare the in silico results with the in vitro experimentations to validate them (or not). Semantic Technologies for Industry 4.0<\/a><\/strong><\/em><\/p>\n Several axes appear in these works (click here<\/a> to access a summary poster):<\/p>\n The \u00ab\u00a0transittability\u00a0\u00bb of a complex biomolecular network expresses the idea of steering a biomolecular network from an undesired condition (generally associated with a disease) to a desired, hopefully, healthy state. In these PhD works, we are interested in the formalisation of these complex biomolecular networks and in the proposal of a platform based on semantic technologies for the optimization of the transittability of these biomolecular networks. These works are led in the framework a PhD thesis under a joint supervision agreement with the University of Tunisia and its\u00a0 LARODEC<\/a> laboratory and also concern a collaboration with the\u00a0CSTB<\/a>\u00a0team of ICube<\/p>\n The increasing availability of High Spatial Resolution satellite images is an opportunity to characterize and identify urban objects. Image analyses methods using object-based approaches based on the use of domain knowledge are necessary to classify data. A major issue in these approaches is domain knowledge formalization and exploitation. The use of formal ontologies seems a judicious choice to deal with these issues. Therefore, the aim of these works is to highlight the benefits in the use of a thematic ontology for automatic regions labelling. Description logics (DL) are being used to exploit the knowledge in the ontologies and develop software tools to assist the automatic labelling of satellite images.<\/p>\n In the framework of these works, I have participated in the\u00a0COCLICO<\/a>\u00a0project funded by the French National Research Agency.<\/p>\n In the framework of the IDEX (excellence initiative) called \u00ab\u00a0Complex Identities\u00a0\u00bb I have worked with the Faculty of Arts of Strasbourg University to try and provide the means to understand the complexity of this institution, with the use of semantic technologies. This collaboration is described in number 27 of the\u00a0#Savoir(s)<\/a>\u00a0journal of May 2016.<\/p>\n","protected":false},"excerpt":{"rendered":" This page presents my former research projects PullCoVapart – Pull the Covid-19 replicative catalytic core apart This is an interdisciplinary project (with two laboratories in Marseille; AFMB and LIS, funded by ANR and INSERM), combining methods in artificial intelligence with … Continuer la lecture
\nThe most important expected contribution in this project is the proposal of a methodology to \u00ab\u00a0translate\u00a0\u00bb hybrid conceptual models (ontologies and reasoning rules) into DEVS simulation models.
\nThe website of the project is http:\/\/pullcovapart.projets.litislab.fr\/<\/a><\/p>\n\n
Formalisation of Complex Biomolecular Networks<\/span><\/strong><\/em><\/h3>\n
Semantic Region Labeling for Remote Sensing Image Interpretation<\/span><\/strong><\/em><\/h3>\n
Semantic Technologies for Improving the Readability and the Comprehensibility of Strasbourg University<\/span><\/strong><\/em><\/h3>\n