Claude Pasquier is researcher at French National Center for Scientific Research (CNRS).
He received a Ph.D. degree in Computer Science from the University of Nice - Sophia Antipolis (now Université Côte d’Azur), France, in 1994. During his thesis, he explored the use of software engineering paradigms in the field of structured document manipulation. He defined both a frame-based language for representing document models, and a system of context-aware document generation and configuration (cf. Prototype-based programming.
Subsequently, he was a postdoctoral researcher at the Biophysics and Bioinformatics Laboratory of the University of Athens, Greece, where he conducted research on protein structure prediction.
He held positions at the National Institute for Research in Digital Science and Technology (INRIA) and with Schlumberger, Smart Cards & Terminals division (now Gemalto) where he worked on generative programming.
Since 2002 when he joined CNRS, he successively worked at Villefranche Oceanographic Laboratory (LOV), the Institute of Biology Valrose (iBV) and New Caledonia Institute of Exact and Applied Sciences (ISEA) where he addressed topics as diverse as semantic data integration, omics data mining and attributed graph mining.
Currently at I3S laboratory, he is conducting research focused on complex network mining that combines computer science and systems biology.
HDR in Computer science, 2018
Université Côte d'Azur
PhD in Computer science, 1994
University of Nice - Sophia Antipolis
Combining itemset mining and structural mining to reveal patterns in attributed graphs.
Multidimensional networks mining in different fields of application.
Semantic integration and analysis of biological data.
Prediction of localization and topology of transmembrane alpha-helices in proteins.
Automatic generation of software tools from specifications.
Dynamic document sharing based on delegation and cloning.
Active Module Identification through Network Embedding.
Mining frequent patterns in attributed graphs.
Triple store with OWL query answering.
Management of Context-Controlled Documents.
Extraction of bi-clusters of genes.
Prediction of alpha-helices in proteins.
Protein sequence analysis on the Web.
Database of proteins' non transmembrane regions.
Database of proteins' transmembrane regions.
Periodicity analysis in molecular sequences.
Java implementation of the Genia tagger.
Mining association rule from gene expression.
Mining frequent patterns in attributed trees.
Mapping XML documents with a set of Java Beans.
Keywords extraction from scientific papers.
Prediction of microRNA-disease associations.
Discretizing gene expression data.
Topology prediction of transmembrane proteins.
Classification of proteins with a neural network.
Prediction of proteins' transmembrane regions.
Identification of transmembrane proteins.
Automatic annotation of genes.
Analysis of gene interaction network.
Semantic data integration.