Ecosim

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Publications


Book chapters

  1. Salehi E., Gras R., Estimation of Distribution Algorithms in Gene Expression Data Analysis In Holmes D. Data Mining: Foundations and Intelligent Paradigms. Volume 3: Medical, Health, Social and Biological and other Applications, 101-121, Springer, 2012.
  2. Shan Y., Gras R., Genome-wide EST data mining approaches to resolving incongruence of molecular phylogenies In Arabnia H.R., Advances in Computational Biology, 237-243, 680 (3), Springer, 2010, Advances in Experimental Medicine and Biology.
  3. Gras R., Study of Genetic algorithms behavior for high epitasis and high dimensional deceptive functions, Evolutionary Computation, 1-20, IN-TECH, 2009, Editor(s) - Wellington Pinheiro dos Santos, Vienna, Austria.
  4. Salehi E., Nyayachavadi J., Gras R., Une étude comparative pour la détection de dépendances
    multiples In Régis Gras, Fabrice Guillet and Jean-Claude Régnier, Analyse Statistique Implicative. Une méthode d'analyse de données pour la recherche de causali, 251-270, 2009, Editor(s) - Cépaduès, Revue des Nouvelles Technologies de l'Information.
  5. Gras, R.; Hernandez, D.; Hernandez, P.; Zangger, N.; Mescam, Y.; Frey, J.; Martin, O.; Nicolas, J.; Appel, R.D., Cooperative metaheuristics for exploring proteomic data, Artificial Intelligence Methods and Tools for Systems Biology, 87 - 106, Springer, 2004.
  6. Gras, R.; Hernandez, P.; Muller, M.; Appel, R.D., Scoring functions for mass spectrometric protein identification, Handbook of Proteomics Methods, 477 - 485, Humana Press, 2003, Editor(s) - Conn, P.M..
  7. Deon, C.; Bienvenut, W.V.; Muller, M.; Gras, R.; Appel, R.D.; Sanchez, J.C.; Hochstrasser, D.F., The Use of Mass Spectrometry in Proteomics, Proteins and Proteomics: A Laboratory Manual, 425 - 596, Cold Spring Harbor Laboratory Press, 2003, Editor(s) - Simpson, R.J..
  8. Bienvenut, W.V.; Muller, M.; Palagi, P.; Heller, M.; Gay, S.; Binz, P.A.; Giron, M.; Gasteiger, E.; Jung, e.; Gras, R.;  Sanchez, J.C.; Appel, r.D.; Hochstrasser, D.F., Proeomics and mass spectrometry: some aspects and recent developments, Spectrometry and Genomic Analysis, 1 - 53, Kluwer Academic Publishers, 2001, Editor(s) - Housby, N..

Journals

  1. Golestani A., Gras R., Cristescu M., Speciation with gene flow in a heterogeneous virtual world: can physical obstacles accelerate speciation?, Proceedings of the Royal Society B: Biological Sciences, 2012, doi: 10.1098/rspb.2012.0466, in press.
  2. Mashayekhi M., Gras R., Investigating the Effect of Spatial Distribution and Spatiotemporal Information on Speciation using Individual-Based Ecosystem Simulation, Journal of Computing, 2(1), 98 – 103.
  3. Shan Y., Gras R., 43 genes support the lungfish-coelacanth grouping related to the closest living relative of tetrapods with the Bayesian method under the coalescence model, BMC Research Notes, , 4:49, 2011, doi:10.1186/1756-0500-4-49.
  4. Golestani A., Gras R., Regularity Analysis of an individual-based Ecosystem Simulation, Chaos: An Interdisciplinary Journal of Nonlinear Science, 20 (043120) 2010.
  5. Soltan Ghoraie L., Gras R., Wang L., Ngom A., Optimal Decoding and Minimal Length for the Non-Unique Oligonucleotide Probe Selection Problem, Neurocomputing, 73(13-15), 2407-2418, 2010.
  6. A. Ngom, L. Rueda, L. Wang and R. Gras, Selection Based Heuristics for the Non-Unique Oligonucleotide Probe Selection Problem in Microarray Design, Special Issue on Pattern Recognition Methods in Bioinformatics, Pattern Recognition Letters, 31(14), 2113-2125, 2010.
  7. Yang Q., Salehi E., Gras R., Using feature selection approaches to find the dependent features, 10th International Conference on Artificial Intelligence and Soft Computing, LNAI 6113(I), 487-494, 2010.
  8. Devaurs D., Gras R., Species abundance patterns in an ecosystem simulation studied through Fisher's logseries, Simulation Modelling Practice and Theory, 18, 100-123, 2010.
  9. Soltan Ghoraie L., Gras R., Wang L., Ngom A., Bayesian Optimization Algorithm for the Non-unique Oligonucleotide Probe Selection Problem, Proceedings of the 4th International Conference on Pattern Recognition in Bioinformatics, LNBI 5780, 365-376, 2009.
  10. Gras R., Devaurs D., Wozniak A., Aspinall A., An individual-based evolving predator-prey ecosystem simulation using a Fuzzy Cognitive Map model of behavior, Artificial Life, 15(4), 423-463, 2009.
  11. Wang L., Ngom A., Gras R., Rueda L., An Evolutionary Approach to the Non-Unique Oligonucleotide Probe Selection Problem, Transactions on Computational System Biology X (LNCS) 5410, 143-162, 2008.
  12. Hernandez, D.; Gras, R.; Appel, R.D., Neighborhood Functions and Hill-Climbing Stragegies dedicated to the Generalized Ungapped Local Multiple alignment, European Journal of Operational Research, 185, 1276-1284, 2008.
  13. Deon, C.; Bienvenut, W.; Sanchez, J.C.; Hochstrasser, D.F.; Muller, M.; Gras, R.; Appel, R.D., Analysis of Proteomes Using the Molecular Scanner, Cold Spring Harbor Protocols(pdb.prot4592), 2007.
  14. Yap, Y.L.; Wong, M.P.; Zhang, X.W.; Hernandez, D.; Gras, R.; Danchin, A., Conserved transcription factor binding sites of cancer markers derived from primary lung adenocarcinoma microarrays, Nucleic Acids Research, 33(1), 409 - 421, 2005.
  15. Frey, J.; Gras, R.; Hernandez, P.; Appel, R.D., A Hierachical Model of Parallel Genetic Proramming Applied to Bioinformatic Problems, Lecture Notes in Computer Science, 3019 Parallel Processing and Applied Mathematics, 1146 - 1153, 2004.
  16. Hernandez, D.; Gras, R.; Apppel, R.D., MoDEL: An efficient strategy for ungapped local multiple alignment, Computational Biology and Chemistry, 28(2), 119 - 128, 2004.
  17. Gras, R.; Hernandez, D.; Hernandez, P.; Zangger, N.; Mescam, Y.; Frey, J.; Martin, O.; Nicolas, J.; Appel, R.D., Cooperative Metaheuristics for exploring proteomic data, Artificial Intelligence Review, 20(1-2), 95 - 120, 2003.
  18. Hernandez, P.; Gras, R.; Frey, J.; Appel, R.D., Popitam: Towards new heuristic strategies to improve protein identification from tandem mass spectrometry data, Proteomics, 3(6), 870 - 879, 2003.
  19. Muller, M.; Gras, R.; Binz, P.A.; Hochstrasser, D.F.; Appel, R.D., Molecular Scanner Experiment with Human Plasma: Improving Protein Identification by Using Intensity Distributions of Matching Peptide Masses, Proteomics, 2(10), 1413 - 1425, 2002.
  20. Muller, M.; Gras, R.; Bienvenut, W.V.; Hochstrasser, D.F.; Appel, R.D., Visualization and Analysis of Molecular Scanner Peptide Mass Spectra, Journal of American Soc. Mass Spectrum, 13, 221 - 231, 2002.
  21. Gras, R.; Muller, M., Computational aspects of protein identification by mass spectrometry, Current Opinion in Molecular Therapeutics, 3(6), 526 - 532, 2001.
  22. Gras, R.; Guillet, R.; Philippe, J., Gras R., Reduction des colonnes d'un tableau de donnees par quasi-equivalence entre variables, Extraction des connaissances et apprentissage, 1(4), 197 - 202, 2001.
  23. Binz, P.A.; Mueller, M.; Walther, D.; Bienvenut, W.V.; Gras, R.; Hoogland, C.; Bouchet, G.; Gasteiger, E.; Fabbretti, R.; Gay, S.; Palagi, P.; Wilings, M.R.; Rouge, V.; Tonella, L.; Paesano, S.; Rossellat, G.; Karming, A.; Bairoch, A.; Sanchez, J.C.; Appel, R.D.; Hochstrasser, D.F., A Molecular Scanner to Automate Proteomic Research and to Display Proteome Images, Analytical Chemisry, 71(21), 4981 - 4988, 1999.
  24. Delamarche, C.; Guerdoux-Jamet, P.; Gras, R.; Nicolas, J., A symbolic-numeric approach to find patterns in genomes: Application to the translation initiation sites of E. coli, Biochimie, 81(11), 1065 - 1072, 1999.
  25. Gras, R.; Mueller, M.; Gas, S.; Gasteiger, E.; Binz, P.A.; Bienvenut, W.; Hoogland, C.; Sanchez, J.C.; Bairoch, A.; Hochstrasser, D.F.; Appel, R.D., Improving protein identification from peptide mass Fingerprinting through a parameterized multi-level scoring algorithm and optimized peak detection, Electrophoresis, 20, 3535 - 3550, 1999.

Conferences

  1. Khater M., Gras R., Adaptation and Genomic Evolution in EcoSim, 12th International Conference on Adaptive Behaviour, in press.
  2. Scott R., Gras R., Comparing Distance-Based Phylogenetic Tree Construction Methods Using An Individual-Based Ecosystem Simulation, EcoSim, The Thirteenth International Conference on the Synthesis and Simulation of Living Systems - Artificial Life 13, in press.
  3. Hosseini M, Gras R., Md Sina, 'Prediction of Imminent Species’ Extinction in EcoSim, International Conference on Agents and Artificial Intelligence 2012, Portugal, 318-323.
  4. Gras R., Majdabadi Farahani Y., Modeling Epidemic Spread in a Predator-Prey Evolutionary Ecosystem Simulation, The Canadian Mathematical Society Winter Meeting 2011.
  5. Mashayekhi M., Gras R., Speciation Prediction based on Spatial Distribution and Spatiotemporal Information from an  Individual-Based Ecosystem Simulation, Advanced Topics in Artificial Intelligence 2011, 56-62.
  6. Khater M., Salehi E., Gras R., Correlation between Genetic Diversity and Fitness in a Predator-Prey Ecosystem Simulation, 24th Australian Joint Conference on Artificial Intelligence, Perth, Australia, 2011, LNAI 7106, 422-431.
  7. Gras R., Golestani A., Hosseini M., Khater M., Majdabadi Farahani Y., Mashayekhi M., Sina M., Sajadi A., Salehi E. and Scott R., EcoSim: an individual-based platform for studying evolution, European Conference on Artificial Life 2011, 284-286.
  8. Salehi E., Gras R., Efficient EDA for Large Opimization Problems via Constraining the Search Space of Models, Gecco (Genetic and Evolutionary Computation Conference) 2011, Dublin, Ireland, 73-74.
  9. Golestani A., Gras R., Multifractal Phenomena in EcoSim, a Large Scale Individual-Based Ecosystem Simulation, ICAI (International Conference on Artificial Intelligence) 2011, Las Vegas, USA, 991-999.
  10. Sina M., Gras R., Computation of population spatial distribution in individual-based ecosystem simulation, IEEE ALIFE 2011, Paris, France, April, in press.
  11. Yang Q., Gras R., How dependencies affect the capability of several feature selection approaches to extract of the key features, ICMLA, Ninth International Conference on Machine Learning and Applications, 127-134, 2010, December.
  12. Majdabadi Farahani Y., Golestani A., Gras R., Complexity and Chaos Analysis of a Predator-Prey Ecosystem Simulation, The second international conference on Advanced Cognitive Technologies and Applications, 52-59, 2010, Lisbon, Portugal, November.
  13. Aspinal A., Gras R., K-Means Clustering as a Speciation Method within an Individual-Based Evolving Predator-Prey Ecosystem Simulation, Lecture Notes in Computer Science, 6335, 318-329, 2010, Toronto,August, IEEE International Conferences on Active Media Technology.
  14. Salehi E., Nyayachavadi J., Gras R., A Statistical Implicative Analysis Based Algorithm and MMPC Algorithm for the Detecting Multiple Dependencie, The 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 10, 22-34, 2010, June.
  15. Saheli E., Gras R., An empirical comparison of the efficiency of several local search heuristics algorithms for Bayesian network structure learning, Learning and Intelligent OptimizatioN IEEE international conference, 13 p, 2009.
  16. Gras R., How efficient are genetic algorithms to solve high epistasis deceptive problems?, 242-249, 2008, May, IEEE World Congress on Computational Intelligence, IEEE World Congress on Computational Intelligence.
  17. Wang L., Ngom A., Gras R., Non-Unique Oligonucleotide Microarray Probe Selection Method Based on Genetic Algorithms, 1004-1011, 2008, May, IEEE World Congress on Computational Intelligence.
  18. Wang L., Ngom A., Gras R., Rueda L., Evolutionary Strategy with Greedy Probe Selection Heuristics for the Non-Unique Oligonucleotide Probe Selection Problem, 54-61, 2008, IEEE Computational Intelligence in Bioinformatics and Computational Biology.
  19. Gras R., Hernandez D., A new Scoring Function for the optimization problem of ungapped local multiple alignement, 2008, Math OPtimization - Theory and Applications.
  20. Gras R., Efficiency of classical and probabilistic model building genetic algorithms on high epistasie deceptive problems, International Conference of the Operational Research Societies Conference (IFORS 2005), 2005, Hawaii  U.S.A.
  21. Hernandez, P.; Gras, R.; Hernandez, C.; Appel, R.D., Peptide identification and characterization using tandem mass spectrometry data: an open-modification search approach with Popitam, Proceedings of: Congress of the Swiss Proteomics Society, 187 - 190, 2005, Zurich, Switzerland, December, Congress of the Swiss Proteomics Society.
  22. Gras, R.; Hernandez, D.; Hernandez, P.; Mescam, Y.; Appel, R.D., Cooperation in metaheuristics and application in bioinformatics, Proceedings of: European Conference on Combinatorial Optimization, 2p, 2004, Bierut, Lebanon,June, European Conference on Combinatorial Optimization.
  23. Hernandez, P.; Gras, R.; Appel, R.D., Popitam loves being fed on modified MS/MS spectra, Peptide Fragmentation and Identified Workshop, Maryland, 2004.
  24. Hernandez, D.; Gras, R.; Appel, R.D., Strategies d'exploration de l'espace des alignements locaux multiples sans indels, Quatrieme journees francophones de recherche operationnelle, 2004.
  25. Hernandez, P.; Gras, R.; Appel, R.D., Popitam squints to better identify MS/MS spectra of modified peptides, Proceedings of: 6th Siena 2D Electrophoresis Meeting, 227, 2004, Siena, Italy, 6th Siena 2D Electrophoresis Meeting.
  26. Frey, J.; Gras, R.; Hernandez, P.; Appel, R.D., A Hieracrchical Model of Coarse-Grained Parallel Genetic Programming, Proceedings of: 5th International Conference on Parallel Processing and Applied Mathematics, 2003, Poland, September, 5th International Conference on Parallel Processing and Applied Mathematics.
  27. Gras, R.; Frey, J.; Hernandez, P.; Appel, R.D., Un modele hierachique 'course-grained' de parallelisation d'algorithme de programmation genetique applique a des problemes de bioinformatique, resolution Parallele des Problemes NPcompletes, Nice, France, 2003, October.
  28. Martin, O.; Gras, R.; Hernandez, D.; Appel, r.D., Optimizing Genetic Algorithms Using Self-Adaptation And Explored Space Modelization, Proceedings of: 5th International Workshop on Frontiers in Evolutionary Algorithms, 291 - 294, 2003, ICIS 2003, North Carolina, September, 5th International Workshop on Frontiers in Evolutionary Algorithms.
  29. Hernandez, P.; Gras, R.; Appel, R.D., Automated Protein Identification from Tandem Mass Spectrometric Data Using Bio-Inspired Algorithms, Proceedings of: Congress of the Swiss Proteomics Society, 81 - 86, 2002, Lausanne, Switzerland, December, Congress of the Swiss Proteomics Society.
  30. Zangger, N.; Gras, R.; Appel, R.D., Biological Sequence Clustering by Cooperative Agents, Proceedings of: Congress of the Swiss Proteomics Society, 75 - 80, 2002, Lausanne, Switzerland, December, Congress of the Swiss Proteomics Society.
  31. Hernandez, P.; Gras, R.; Appel, R.D., Automated protein identification from tandem mass spectrometric data using bio-inspired algorithms, Proceedings of: 5th Siena 2D electrophoresis meeting, 4p, 2002, Siena, Italy, 5th Siena 2D electrophoresis meeting.
  32. Binz, P.A.; Muller, M.; Hoogland, C.; Walther, D.; Gay, S.; Gras, R.; Vienvenut, W.V.; Pasquarello, C.; Paesano, S.; Corthais, G.; Sanchez, J.C.; Bairoch, A.; Hochstrasser, D.F.; Appel, R.D., Danish Society for Biochemistry of
    Molecular Biology, Danish Society for Biochemistry of Molecular Biology, The Synergy between Bioinformatics and Proteomica Research, 2002, October.
  33. Appel, r.D.; Hernandez, P.; Tuloup, M.; Walther, D.; Gras, r.; Muller, M.; Hoogland, C.; Binz, P.A.; Corthals, G.L.; Sanchez, J.C.; Hochstrasser, D.F., First Annual HUPO Congress, Versailles, France, New developments in proteomatics at the Swiss Institute of Bioinformatics, 2002, October.
  34. Gras, R.; Muller, M.; Hernandez, D.; Hernandez, P.; Zangger, N.; Palagi, P.; Binz, P.A.; Lisacek, F.; Appel, r.D., New approaches of computational proteomics, Proceedings of: JOBIM, 143 - 147, 2002, St. Malo, France, JOBIM.
  35. Zangger, N.; Gras, R.; Appel, R.D., A Symmetrick Cooperative Metaheuristic for Biological Sequence Clustering Using Local Entrophy as Similary Criterion, NETTAB, Bologna, Italia, 2002, poster presentation.
  36. Hernandez, P.; Gras, R.; appel, r.D., Ant colony Optimization Metaheuristic Applied to Automated Protein Identification from Tandem Mas Spectrometric Data, NETTAB, Bologna, Italia, 2002, poster presentation.
  37. Hernandez, D.; Gras, R.; Lisacek, R.; Appel, R.D., MoDEL: Inference de motifs avec un algorithme evolutionniste, JOBIM, 265 - 267, 2002.
  38. Nadler, T.; Huang, Y.; Wagenfeld, B.; Parker, B.; Lotti, R.; Vella, G.J.; Binz, P.A.; Muller, M.; Gras, R.; Hochstrasser, D.F.; Bienvenut, W.; Sanchez, J.C.; Corthais, G.; Appel, R.D., Proteome Analysis by Multi-Dimensional Liquid Chromatography Gel-Electrophoresis and the Molecular Scanner, Proceedings of: 5th Siena 2D electrophoresis meeting, 2p, 2002, Siena, Italy, 5th Siena 2D electrophoresis meeting.
  39. Muller, M.; Gras, R.; Appel, r.D., Using peptide signal intensity distributions for better interpretation of molecular scanner data, Proceedings of: 5th Siena 2D electrophoresis meeting, 2p, 2002, Siena, Italy, 5th Siena 2D electrophoresis meeting.
  40. Gras, R.; Muller, M.; Hernandez, D.; Hernandez, P.; Zangger, N.; Palagi, P.; Binz, P.A.; Appel, R.D., Cooperative metaheuristic for proteomics, ISMIS, 2002, poster presentation.
  41. Hernandez, D.; Gras, R.; Appel, R.D., Automated learning of unknown length motifs in unaligned biological sequences with genetic algorithm, Proceedings of: Congress of the Swiss Proteomics Society, 55 - 57, 2001, Congress of the Swiss Proteomics Society.
  42. Muller, M.; Gras, R.; Bienvenut, W.V.; Hochstrasser, D.F.; Appel, R.D., Visualisation and Analysis of Molecular Scanner Peptide Mass Spectra, Proceedings of: Congress of the Swiss Proteomics Society, 39 - 43, 2001, Congress of the Swiss Proteomics Society.
  43. Hernandez, D.; Gras, R.; Appel, R.D., Automated learning of unknown length motifs in unaligned DNa sequences with genetic algorithms, The 9th International Conference on Intelligent Systems for Molecular Biology, 2001, July, Copenhagen, Denmark, poster presentation.
  44. Muller, M.; Gras, R.; Appel, r.D., Vizualization and Interpretation of the Molecular Scanner Date, the 9th International conference on Intelligent Systems for Molecular Biology, 2001, July, Copenhagen, Denmark, poster présentation.
  45. Appel, R.D.; Walther, D.; Binz, P.A.; Hoogland, Ch.; Fabbretti, R.; Palegi, P.; Bouchet, G.; Gras, R.;Mueller, M.; Gay, S.; Gasteiger, E.; Bairoch, A.; Sanchez, J.C.; Hochstrasser, D.F., High-throughput proteomics: the need for highperformance computing, 27th SPEEDUP Workshop, Lugano, Switzerland, 2000, March.
  46. Bienvenut, W.V.; Muller, M.; Paesano, S.; Gras, R.; Binz, P.A.; Converset, V.; Deon, C.; Appel, R.D.; Sanchez, J.C.; Hochstrasser, D.F., Comprehensive proteome analysis: the molecular scanner, Proceedings of: Congres commun des societes de biochimie francaise et italienne, 2p, 2000, Siena, Italy, September, Congres commun des societes de biochimie francaise et italienne.
  47. Binz, P.A.; Gasteiger, E.; Gras, R.; Hoogland, C.; Muller, M.; Walther, D.; Bienvenut, W.; Zimmermann, C.; Sanchez, J.C.; Bairoch, A.; Appel, R.D.; Hochstrasser, D.F., Third Millenium CCL, Milano, Italia, Informatics of Proteomics and Possible Use in Laboratory Sciences. Computation in Clinical Laboratory, 2000, September.
  48. Hochstrasser, D.F.; Bienvenut, W.; Mueller, M.; Gras, R.; Binz, P.A.; Appel, R.D.; Sanchez, J.C., From Genome to Proteome: the development of a molecular scanner, Grenoble, France, Colloque AcM/MdA - Acquisition conduite par le Modele - Model driven Acquisition, 2000, November.
  49. Gras, R.; Gasteiger, E.; Chopard, B.; Mueller, M.; Appel, R.D., New learning method to improving protein identification from peptide mass fingerprinting, Proceedings of: 4th Siena 2D electrophoresis meeting, 13p, 2000, Siena, Italy, September, 4th Siena 2D electrophoresis meeting.
  50. Gras, R.; Gras, R.; Gasteiger, E.; Binz, P.A.; Mueller, M.; Chopard, B.; Appel, R.R., Classification automatique par algorithme génétique dans le cadre de l'identification par empreinte de masse peptidique, Journées Fouille dans les données par la méthode d'analyse statistique implicative, 59 - 84, 2000, Caen, France.
  51. Palagi, P.M.; Hoogland, Ch.; Gras, R.; Mueller, M.; Binz, P.A.; Fabbretti, R.; Bouchet, G.; Walther, D.; Gay, S.; Gasteiger, E.; Bairoch, A.; Sanchez, J.C.; Hochstrasser, D.F.; Appel, R.D., Human Proteomics and bioinformatics tools, Berlin, Germany, The first draft of the human genome: an academic and industrial perspective, 2000.
  52. Gras, R.; Gasteiger, E.;Mueller, M.; Appel, R.D., New learning method to improve protein identification from peptide mass fingerprinting, The 8th International Conference on Computational Biology Intelligent Systems for Molecular Biology, 2000, La Jolla, CA U.S.A, poster presentation.
  53. Bienvenut, W.V.; Heller, M.; Mueller, M.; Paesano, S.; converset, V.; Gras, R.; Binz, P.A.; Sanchez, J.C.; Appel, R.D.; Hochstrasser, D.E., Comprehensive one step aanalysis by the molecular scanner: quantitative evaluation, Proceedings of: 4th Siena 2D electrophoresis meeting, 2p, 2000.
  54. Hochstrasser, D.F.; Binz, P.A.; Bienvenut, w.; Mueller, M.; Walther, D.; Gras, R.; Hoogland, Ch.; Bouchet, g.; Gasteiger, E.; Fabbretti, R.; Gay, s.; Palagi, P.; Wilkins, M.; Rouge, V.; Tonella, L.; Paesano, S.; Rossellat, G.; Karmine, A.; Bairoch, a.; Apel, R.D.; Sanchez, J.C., Elektrophorese Forum '99, Munich, Germany, Proteomics Technology Development in Geneva, 1999, October.
  55. Hochstrasser, D.F.; Sanchez, J.C.; Binz, P.A.; Bienvenut, W.; Gasteiger, E.; Muller, M.; Gras, R.; Fabretti, R.; Appel, R.D., 31at annual Meeting of USGEB, The role of the virtual or computer "dry' laboratory in proteomics, 1999.
  56. Gras, R., Recherche d'association de motifs approches dans les grandes sequences genetiques, Proceedings of: RFIA'98, III, 355 - 364, 1998, Clermont-Ferrand, France, RFIA'98.
  57. Gras, R., Multiple searches of association of flexible regular expressions in large genetic sequences, Proeedings of: Mathematical analysis of biological sequences, 13p, 1997, Rouen, France, Mathematical analysis of biological sequences.
  58. Gras, R.; Nicolas, J., Forest: A Browser for huge DNa sequences, Proceedings of: Seventh Workshop on Genome Informatics, 147 - 156, 1996, Tokyo, Japan, December, Seventh Workshop on Genome Informatics.
  59. Gras, R.; Nicolas, J., Caractérisation de séquences biologiques. Définition d'un outil d'analyse pour les grandes sequences, Rencontre Jeunes Chercheurs en Intelligence Artificielle, 263, 1996.
  60. Gras, R., FOuineur de REpetitions dans les sequences Titanesques, Proceedings of: Journees Seminaire Junior en Intelligence Artificelle, 1996, Paris, France, Journees Seminaire Junior en Intelligence Artificelle.
  61. Gras, R.; Nicolas, J., FOREST, browser of repeats in huge sequences., The 3rd Intenational Conference on Computational Biology Intelligent Systems for Molecular Biology, 1995, Cambridge, England, poster presentation.

Invited seminars

  • 2010
    • University of Guelph
    • University of Windsor
  • 2008
    • Great Lakes Institute for Environmental Research Windsor
    • Hong Kong Baptist University
  • 2007
    • University of Toronto
  • 2006
    • University of Western Ontario
    • University of Orsay
    • University of Ottawa
    • University of Windsor
    • University of Lille
  • 2005
    • University of Concordia
    • University of Bordeaux
    • Institut des Hautes Etudes Scientifiques, Paris
  • 2004
    • University of Le Havre
    • Pasteur Institute, Paris
    • Curie Institute
    • INRIA Nancy
    • University of Lausanne
    • Mathematic Institute of Luminy, Marseille
  • 2003
    • IDSIA Lugano
    • University of Valenciennes
  • 2002
    • INRIA Grenoble
    • IRESTE Nantes
  • 2001
    • University of Geneva
  • 2000
    • IRESTE Nantes
  • 1998
    • University of Montreal
    • Swiss Institute of Bioinformatics