Interspecies translation of disease networks increases robustness and predictive accuracy, PLoS Comput. Biol, vol.7, 2011. ,
Web monitoring of emerging animal infectious diseases integrated in the French Animal Health Epidemic Intelligence System, PLoS One, vol.13, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-02629163
Predicting reservoir hosts and arthropod vectors from evolutionary signatures in RNA virus genomes, Science, vol.362, pp.577-580, 2018. ,
Hidden Markov phylogenetic models offer an interesting perspective to identify "high risk lineages" of environmental pathogens, Infection, Genet. Evol, vol.55, pp.45-47, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01605601
Introduction to Computational Immunology, Computational Immunology: Models and Tools. Bassaganya-Riera J, 2016. ,
Controlling bovine paratuberculosis at a regional scale: towards a decision modeling tool, J. Theor. Biol, vol.435, pp.157-183, 2017. ,
Automated analysis of free speech predicts psychosis onset in high-risk youths, vol.1, p.15030, 2015. ,
Support vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors, Geoinformatica, vol.20, pp.693-714, 2016. ,
A framework to promote collective action within the One Health community of practice: using participatory modelling to enable interdisciplinary, cross-sectoral and multi-level integration, One Health, vol.1, pp.44-48, 2015. ,
The Impact of Movements and Animal Density on Continental Scale Cattle Disease Outbreaks in the United States, PLoS One, vol.9, 2014. ,
KENDRICK: A Domain Specific Language and platform for mathematical epidemiological modelling, proc. IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future, pp.132-137, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01377090
Optimization of prion assemblies fragmentation, Proc. IEEE Conf. Decision Control, Presented at 55 th IEEE Conf. Decision Control (CDC), 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01607567
Comité consultatif commun d'éthique pour la recherche agronomique sur le bien-être des animaux d' élevage, vol.26, 2015. ,
Exploratory analysis of methods for automated classification of laboratory test orders into syndromic groups in veterinary medicine, PLoS One, vol.8, p.57334, 2013. ,
Issues and special features of animal health research, Vet. Res, vol.42, p.96, 2011. ,
URL : https://hal.archives-ouvertes.fr/hal-01191150
Characteristics of the spatio-temporal network of cattle movements in France over a 5-year period, Prev. Vet. Med, vol.117, pp.79-94, 2014. ,
URL : https://hal.archives-ouvertes.fr/hal-02635459
Modélisation du risque épidémiologique : vers des outils d'aide à la gestion. Cas de la maîtrise de la diarrhée virale bovine (BVDV) en troupeau bovin allaitant, Journées Nationales des Groupements Techniques Vétérinaires (JNGTV), pp.187-192, 2015. ,
Gestion des maladies endémiques du troupeau aux territoires : contribution de la modélisation épidémiologique pour soutenir la prise de décision (projet MIHMES, Innov. Agro, vol.68, pp.53-65, 2018. ,
Interdisciplinarity and infectious diseases: an Ebola case study, PLoS Pathogens, vol.11, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01223813
Modelling Salmonella transmission among pigs from farm to slaughterhouse: interplay between management variability and epidemiological uncertainty, Int. J. Food Microbiol, vol.229, pp.33-43, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01334840
Spatial risk mapping for rare disease with hidden Markov fields and variational EM, Annals Appl, 2013. ,
URL : https://hal.archives-ouvertes.fr/inria-00577793
, Stat, vol.7, pp.1192-1216
How to prevent viremia rebound? Evidence from a PRRSv data-supported model of immune response, BMC Systems Biol, vol.13, p.15, 2017. ,
Livestock metabolomics and the livestock metabolome: A systematic review, PLoS ONE, vol.12, 2017. ,
The United-Nations, Health and Sustainability. An analysis of Sustainable Development Goal #3 "Health and well-being, Veterinaria Mexico AO, 2018. ,
Confronting data sparsity to identify potential sources of Zika virus spillover infection among primates, Epidemics, vol.27, pp.59-65, 2019. ,
Dynamical Network Models for Cattle Trade: Towards Economy-Based Epidemic Risk Assessment, J. Complex Networks, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-02627144
Artificial intelligence in radiology, Nature Rev. Cancer, vol.18, pp.500-510, 2018. ,
Using natural language processing and VetCompass to understand antimicrobial usage patterns in Australia, Aust. Vet. J, 2019. ,
Validation of text-mining and content analysis techniques using data collected from veterinary practice management software systems in the UK, Prev. Vet. Med, vol.167, pp.61-67, 2019. ,
Integrative omics for health and disease, Nature Rev. Genet, vol.19, pp.299-310, 2018. ,
Modélisation multi-agents pour la gestion individuelle et collective d'une maladie infectieuse, Proc. 26 e Journées Francophones sur les Systèmes Multi-Agents (JFSMA), p.1979509, 2018. ,
Opinion: Reproductible research can still be wrong: adopting a prevention approach, Proc. Natl. Acad. Sci, vol.112, pp.1645-1646, 2015. ,
Artificial Neuronal Networks. Application to Ecology and Evolution, 2000. ,
,
, Machine Learning in Agriculture: A Review. Sensors, vol.18, 2018.
Artificial intelligence systems for complex decision-making in acute care medicine: a review, Patient Safety in Surgery, vol.13, 2019. ,
Can routinely recorded reproductive events be used as indicators of disease emergence in dairy cattle? An evaluation of 5 indicators during the emergence of bluetongue virus in France in 2007 and, J. Dairy Sci, vol.97, pp.6135-6150, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-02640286
Multi-level agent-based simulations: Four design patterns, Simul. Model. Pract. Theory, vol.83, pp.51-64, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01691388
Big data analytics and precision animal agriculture symposium: machine learning and data mining advance predictive big data analysis in precision animal agriculture, J. Anim. Sci, vol.96, pp.1540-1550, 2018. ,
Broadwick: a framework for computational epidemiology, BMC Bioinformatics, vol.17, 2016. ,
A web-based system for near real-time surveillance and space-time cluster analysis of foot-and-mouth disease and other animal diseases, Prev. Vet. Med, vol.91, pp.39-45, 2009. ,
Spatial and temporal epidemiological analysis in the Big Data era, Prev. Vet. Med, vol.122, pp.213-220, 2015. ,
DiFFuSE, a distributed framework for cloud-based epidemic simulations: a case study in modelling the spread of bovine viral diarrhea virus, Int. Conf. Cloud Comp. Technol. Sci., Presented at 9 th IEEE Int. Conf. Cloud Comp. Technol. Sci., (CloudCom), pp.304-313, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01586945
A Multi-Level Multi-Agent Simulation Framework in Animal Epidemiology, Proc. 15 th Int. Conf. Practical Appl. Agents and Multi-Agent Systems (PAAMS), pp.209-221, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01536640
Combining early hyperthermia detection with metaphylaxis for reducing antibiotics usage in newly received beef bulls at fattening operations: a simulation-based approach, Soci. Veter. Epidemiol. Preventive Medicine (SVEPM), 13 pages. Utrecht, The Netherland, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01987110
EMULSION: transparent and flexible multiscale stochastic models in human, animal and plant epidemiology, PLoS Computat. Biol, vol.15, p.1007342, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02288201
5, 1. Prospective, 2019. Intelligence artificielle : état de l'art et perspective pour la France, Health Inf. Sci. Syst, 2017. ,
Neighbourhood contacts and trade movements drive the regional spread of bovine viral diarrhoea virus (BVDV), Vet. Res, vol.50, p.30, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02120535
Artificial intelligence-enabled healthcare delivery, J. Roy. Soc. Med, vol.112, pp.22-28, 2019. ,
Multi-agent systems in epidemiology: a first step for computational biology in the study of vector-borne disease transmission, BMC Bioinformatics, vol.9, 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-02392523
Artificial Intelligence A Modern Approach. Third Edition, p.1132, 2010. ,
Ten simple rules for reproducible computational research, PLOS Comput. Biol, vol.9, 2013. ,
Better medicine through machine learning: What' s real, and what' s artificial?, PLOS Med. Blog "Speaking of Medicine, 2018. ,
Mind and machine in drug design, Nature Machine Intelligence, vol.1, pp.128-130, 2019. ,
Inferring an ontology of single cell motions from high-throughput microscopy data, Proc. IEEE Int. Symp. Biomed. Imaging, pp.160-163, 2015. ,
URL : https://hal.archives-ouvertes.fr/hal-01246694
Scalable network analytics for characterization of outbreak influence in voluminous epidemiology datasets, Concurrency Comput. Practice Exp, vol.31, 2019. ,
Multi-Agent System Applications in Healthcare: Current Technology and Future Roadmap, Procedia Computer Sci, vol.52, pp.252-261, 2015. ,
Multi-omic data integration and analysis using systems genomics approaches: methods and applications in animal production, health and welfare, Genet. Sel. Evol, vol.48, p.38, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01341372
The Impact of Farmers' Strategic Behavior on the Spread of Animal Infectious Diseases, PLoS ONE, vol.11, 2016. ,
URL : https://hal.archives-ouvertes.fr/hal-01565523
ASICS: an automatic method for identification and quantification of metabolites in complex 1D 1H NMR spectra, Front Vet Sci, vol.13, p.110, 2017. ,
URL : https://hal.archives-ouvertes.fr/hal-01535613
A modelling framework based on MDP to coordinate farmers' disease control decisions at a regional scale, PLOS ONE, vol.13, 2018. ,
URL : https://hal.archives-ouvertes.fr/hal-01815389
Donner un sens à l'intelligence artificielle : pour une stratégie nationale et européenne, 2018. ,
Review Solving Immunology?, Trends Immunol, 2016. ,
Artificial Intelligence for Advanced Problem Solving Techniques, Infor mation Science Reference, p.369, 2008. ,
Network-based machine learning and graph theory algorithms for precision oncology, NPJ Precision Oncology, vol.1, p.25, 2017. ,
, IA peut dans certaines situations faciliter le diagnostic et la détection de cas, fiabiliser les prédictions et réduire les erreurs, permettre des représentations plus réalistes et lisibles par des non informaticiens de systèmes biologiques complexes, accélérer les décisions, améliorer la précision des analyses de risque et permettre de mieux cibler les interventions et d'en anticiper les effets. De plus, les fronts de science en SA engendrent de nouveaux challenges pour l'IA, du fait de la spécificité des systèmes, des données, des contraintes, et des objectifs d'analyse. Sur la base d'une revue de la littérature à l'interface entre IA et SA couvrant la période 2009-2019, et d'entretiens conduits avec des chercheurs français positionnés à cette interface, cette synthèse explicite les grands domaines de recherche en SA dans lesquels l'IA est actuellement mobilisée, comment elle contribue à revisiter les questions de recherche en SA et lever des verrous méthodologiques, et comment des questions de SA stimulent de nouveaux travaux en IA. Après avoir présenté les freins et leviers possibles, Résumé Mobiliser les approches issues de l'Intelligence Artificielle (IA) en Santé Animale (SA) permet d'aborder des problèmes de forte complexité logique ou algorithmique tels que rencontrés en épidémiologie quantitative et prédictive, en médecine de précision, ou dans l'étude des relations hôtes × pathogènes. L'