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Claude Pasquier

Researcher in Computer Science / Computational Biology

Université Côte d'Azur

CNRS

I3S laboratory

Biography

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.

Interests

  • Data Mining
  • Machine Learning
  • Computational Biology

Education

  • HDR in Computer science, 2018

    Université Côte d'Azur

  • PhD in Computer science, 1994

    University of Nice - Sophia Antipolis

Projects

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Attributed Graph Mining

Combining itemset mining and structural mining to reveal patterns in attributed graphs.

Complex Network Mining

Multidimensional networks mining in different fields of application.

Omics Data Mining

Semantic integration and analysis of biological data.

Protein Structure Prediction

Prediction of localization and topology of transmembrane alpha-helices in proteins.

Generative Programming

Automatic generation of software tools from specifications.

Prototype-based Programming

Dynamic document sharing based on delegation and cloning.

Recent Publications

Population-based meta-heuristic for active modules identification

The identification of condition specific gene sets from transcrip- tomic experiments has important biological applications, ranging …

Prediction of miRNA-disease Associations using an Evolutionary Tuned Latent Semantic Analysis.

MicroRNAs, small non-coding elements implied in gene regulation, are very interesting biomarkers for various diseases such as cancers. …

Softwares

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AADAGE

Mining frequent patterns in attributed graphs.

AllOnto

Triple store with OWL query answering.

Bible

Management of Context-Controlled Documents.

CGGA

Extraction of bi-clusters of genes.

CoPreThi

Prediction of alpha-helices in proteins.

Dam-Bio

Protein sequence analysis on the Web.

DB-NTMR

Database of proteins’ non transmembrane regions.

DB-TMR

Database of proteins’ transmembrane regions.

FT

Periodicity analysis in molecular sequences.

GeniaJ

Java implementation of the Genia tagger.

GenMiner

Mining association rule from gene expression.

IMIT

Mining frequent patterns in attributed trees.

KeTuK

Mapping XML documents with a set of Java Beans.

KeyStract

Keywords extraction from scientific papers.

MiRAI

Prediction of microRNA-disease associations.

NorDi

Discretizing gene expression data.

OrienTM

Topology prediction of transmembrane proteins.

PRED-CLASS

Classification of proteins with a neural network.

PRED-TMR

Prediction of proteins’ transmembrane regions.

PRED-TMR2

Identification of transmembrane proteins.

THEA

Automatic annotation of genes.

THEA-INTERACT

Analysis of gene interaction network.

THEA-ONLINE

Semantic data integration.

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