Learning linguistic models and application to biological sequences

Keywords: Grammatical Inference, Machine Learning, Functions and Structures of Protein Sequences, DNA…


Protomata Learner infers automata to model (even heterogenous) families of protein sequences.
The current version, Protomata 2.0 can be used through a web interface on the Genouest Bioinformatics platform server.
We are working on version 3.0…

PPalign, Potts to Potts alignment of protein sequences taking into account residues coevolution, with Hugo Talibart.

PhD Students

Previous PhD Students

Funded projects

  • Pepper: Vers la nouvelle génération de méthodes d’alignement protéiques avec les modèles de Potts, coordinated by Mathilde Carpentier, Émergence 2021-2022 from Alliance Sorbonne Université
Previous funded projects:
  • IDEALG Seaweed for the future, ANR Investissements d’avenir, Biotechnology and Bioressource
  • Characterization of desaturases with Pleiade team, IPL Algae in silico
  • Grammatical inference methods in classification of amyloidogenic proteins with Politechnika Wroclawska, Polland, funded by Polish National Science Center
  • “Omics”-Line of the Chilean CIRIC-Inria Center
  • PEPS project: Characterisation and identification of viral sequences in marine metagenomes
  • ANR Biotempo: Languages, time representations and hybrid models for the analysis of incomplete models in molecular biology
  • ANR LepidOLF: Microgénomique de la sensille phéromonale d’un lépidoptère : une approche novatrice pour comprendre les mécanismes olfactifs et leur modulation
  • ANR Pelican : Competing for light in the ocean: An integrative genomic approach of the ecology, diversity and evolution of cyanobacterial pigment types in the marine environment
  • Collaboration MINCyT (ex SECyT) – INRIA with the  “Grupo de Procesamiento de Lenguaje Natural ” of Gabriel Infante-Lopez: Modélisation linguistique de séquences génomiques par apprentissage de grammaires
  • ANR Proteus: Reconnaissance de pli et repliement inverse : vers une prédiction à grande échelle des structures de protéines
  • ANR Modulome: Deciphering and modelling the structural organization of genomes

Selected publications

Primers and reviews
Looking at long distance correlations
Residues coevolution
Protein sequences and structures
Learning context-free grammars
Learning automata
  • Extracellular vesicles produced by human and animal Staphylococcus aureus strains share a highly conserved core proteome, Natayme Rocha Tartaglia, Aurélie Nicolas, Vinícius de Rezende Rodovalho, Brenda Silva Rosa da Luz, Valérie Briard-Bion, Zuzana Krupova, Anne Thierry, François Coste, Agnes Burel, Patrice Martin, Julien Jardin, Vasco Azevedo, Yves Le Loir, Eric Guédon. Scientific Reports, Nature Publishing Group, 2020
  • CyanoLyase: a database of phycobilin lyase sequences, motifs and functions, Anthony Bretaudeau, François Coste, Florian Humily, Laurence Garczarek, Gildas Le Corguillé, Christophe Six, Morgane Ratin, Olivier Collin, Wendy M Schluchter, Frédéric Partensky. Nucleic Acids Research, Oxford University Press, 2012
  • Learning Automata on Protein Sequences, François Coste and Goulven Kerbellec, JOBIM 2006 (abstract, paper, slides, dataset).
  • A Similar Fragments Merging Approach to Learn Automata on Proteins, François Coste and Goulven Kerbellec, ECML 2005. (abstract, paper, extended version, data sets).
    Some slides presenting this work and more at a grammatical inference workshop: slides, 4 per pages for printing
  • Introducing Domain and Typing Bias in Automata Inference, François Coste, Daniel Fredouille, Christopher Kermorvant and Colin de la Higuera. ICGI 2004. paper (.pdf), slides (.ppt, 2.2MB)
  • Mutually compatible and incompatible merges for the search of the smallest consistent DFA, John Abela, François Coste and Sandro Spina. ICGI 2004. paper (.pdf), slides (.ppt)
  • Unambiguous automata inference by means of state-merging methods. François Coste, Daniel Fredouille, ECML’03. paper (.ps.gz, .pdf) complementary experiments (.ps.gz, .pdf), benchmarks (.tar.gz), slides (.ppt).
    Parsing ambiguity!
  • What is the Search Space for the Inference of Non Deterministic, Unambiguous and Deterministic Automata ? François Coste, Daniel Fredouille, Techn. Report, RR-4907, 2003
  • Efficient ambiguity detection in C-NFA, a step toward inference of non deterministic automata, François Coste, Daniel Fredouille, ICGI 2000, Grammatical inference: algorithms and applications, Lisbonne , 25-38 , september , 2000. paper (.ps.gz, .pdf) benchmark (.tar.gz).
    Classification ambiguity!
  • State merging inference of finite state classifiers, François Coste, INRIA/IRISA, May 1999, report (.ps.gz, .pdf)
  • Regular Inference as a graph coloring problem, François Coste, Jacques Nicolas, ICML97, Grammatical Inference Workshop, Nashville TN, USA, 1997 (.ps.gz, .pdf)

Ph.D. Thesis

Grammatical Inference Benchmarks and Competitions

  • I gathered classical grammatical inference benchmarks in this GIB repository. Don’t hesitate to contribute with your own data sets, especially real world ones!
  • I set up the Gowachin server, a continuation of the Abbadingo One DFA learning competition, allowing to generate parametrized problems. I also co-organized Omphalos, the competition on learning context-free languages, which is now over but the data sets are still available… If you are interested in grammatical inference competitions, look at this page.

More complete list of publication here.
This page is updated on an irregular basis: browse HAL for new publications.

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