Arnaud Ferré

Arnaud Ferré
  • PhD & General engineering MSc & Bioinformatics MSc
  • Researcher at University of Paris-Saclay

About

19
Publications
4,875
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160
Citations
Introduction
Arnaud Ferré currently works at Montréal University as postdoctoral researcher. Arnaud does research in artificial intelligence, information extraction and knowledge representation, mainly for life sciences application.
Current institution
University of Paris-Saclay
Current position
  • Researcher
Additional affiliations
October 2015 - September 2018
University of Paris-Saclay
Position
  • PhD Student

Publications

Publications (19)
Conference Paper
Full-text available
This paper presents the Bacteria Biotope task of the BioNLP Shared Task 2016, which follows the previous 2013 and 2011 editions. The task focuses on the extraction of the locations (biotopes and geographical places) of bacteria from PubMed abstracts and the characterization of bacteria and their associated habitats with respect to reference knowled...
Article
Full-text available
Human body odor is produced when sweat-secreted compounds are metabolized by bacteria present on the skin. The resulting volatile mixture is often negatively perceived, motivating the use of personal cosmetic deodorants. Yet body odor may also be positively perceived in some contexts, and is proposed to play a role in sexual attraction, kin identif...
Article
Full-text available
Background High-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and methods such as bio-ontologies to extract and share valuable information. In parallel, the development of re...
Chapter
This paper presents an enhanced approach for adapting a Language Model (LM) to a specific domain, with a focus on Named Entity Recognition (NER) and Named Entity Linking (NEL) tasks. Traditional NER/NEL methods require a large amounts of labeled data, which is time and resource intensive to produce. Unsupervised and semi-supervised approaches overc...
Article
Full-text available
Background Entity normalization is an important information extraction task which has recently gained attention, particularly in the clinical/biomedical and life science domains. On several datasets, state-of-the-art methods perform rather well on popular benchmarks. Yet, we argue that the task is far from resolved. Results We have selected two go...
Article
Full-text available
Background Entity normalization is an important information extraction task which has gained renewed attention in the last decade, particularly in the biomedical and life science domains. In these domains, and more generally in all specialized domains, this task is still challenging for the latest machine learning-based approaches, which have diffi...
Article
Full-text available
Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. P...
Thesis
L'augmentation considérable de la quantité des données textuelles rend aujourd’hui difficile leur analyse sans l’assistance d’outils. Or, un texte rédigé en langue naturelle est une donnée non-structurée, c’est-à-dire qu’elle n’est pas interprétable par un programme informatique spécialisé, sans lequel les informations des textes restent largement...
Article
Full-text available
iGEM (pour international genetically engineered machine) est un concours international autour de la biologie synthétique réunissant des étudiants de toutes disciplines (mathématiques, physique, biologie, arts, etc.). « L’objectif est de construire un système biologique fonctionnel complexe, en assemblant des composants individuels moléculaires simp...
Article
Full-text available
We present a new educational initiative called Meet-U that aims to train students for collaborative work in computational biology and to bridge the gap between education and research. Meet-U mimics the setup of collaborative research projects and takes advantage of the most popular tools for collaborative work and of cloud computing. Students are g...

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