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Joint European Conference on Machine Learning and Knowledge Discovery in Databases

ECML PKDD 2021: Machine Learning and Principles and Practice of Knowledge Discovery in Databases pp 459–467Cite as

Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-Based SVM

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1525))

Abstract

A crucial step to assure drug safety is predicting off-target binding. For oligonucleotide drugs this requires learning the relevant thermodynamics from often large-scale data distributed across different organisations. This process will respect data privacy if distributed and private learning under limited and private communication between local nodes is used. We propose an ADMM-based SVM with differential privacy for this purpose. We empirically show that this approach achieves accuracy comparable to the non-private one, i.e. \({\sim }86\%\), while yielding tight empirical privacy guarantees even after convergence.

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Notes

  1. 1.

    Here, M is the number of nodes across which the data is distributed and \(M \le N\).

  2. 2.

    E.g. the 3-gram “GCG” has larger weight due to higher binding affinity than “ATA”.

  3. 3.

    Increase in privacy level \(\epsilon \) indicates decrease in differential privacy.

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Acknowledgments

SSF Strategic Mobility Grant “Drug Discovery for Antisense Oligos” (A.S.), Swedish National Supercomputer Centre (A.S. & S.T.).

Author information

Authors and Affiliations

  1. CSE, University of Gothenburg | Chalmers University of Technology, Gothenburg, Sweden

    Shirin Tavara & Alexander Schliep

  2. Équipe Scool, Inria, UMR 9189-CRIStAL, CNRS, Univ. Lille, Centrale Lille, Lille, France

    Debabrota Basu

Authors
  1. Shirin Tavara
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  2. Alexander Schliep
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  3. Debabrota Basu
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Corresponding author

Correspondence to Shirin Tavara .

Editor information

Editors and Affiliations

  1. IKIM, Ruhr-University Bochum, Bochum, Germany

    Michael Kamp

  2. University of Sydney, Sydney, NSW, Australia

    Irena Koprinska

  3. University of Namur, Namur, Belgium

    Adrien Bibal

  4. University of Rennes 1, Rennes, France

    Tassadit Bouadi

  5. University of Namur, Namur, Belgium

    Benoît Frénay

  6. Inria, Rennes, France

    Luis Galárraga

  7. University of Antwerp, Antwerp, Belgium

    José Oramas

  8. Ruhr University Bochum, Bochum, Germany

    Linara Adilova

  9. Royal Holloway University of London, Egham, UK

    Yamuna Krishnamurthy

  10. Ghent University, Ghent, Belgium

    Bo Kang

  11. Université Jean Monnet, Saint-Etienne cedex 2, France

    Christine Largeron

  12. Ghent University, Gent, Belgium

    Jefrey Lijffijt

  13. Telecom Paris, Paris, France

    Tiphaine Viard

  14. University of Bonn, Bonn, Germany

    Pascal Welke

  15. Norwegian Univesity of Science and Technology, Trondheim, Norway

    Massimiliano Ruocco

  16. BI Norwegian Business School, Oslo, Norway

    Erlend Aune

  17. University of Pisa, Pisa, Italy

    Claudio Gallicchio

  18. University of Duisburg-Essen, Essen, Germany

    Gregor Schiele

  19. Graz University of Technology, Graz, Austria

    Franz Pernkopf

  20. Xilinx Research, Dublin, Ireland

    Michaela Blott

  21. Heidelberg University, Heidelberg, Germany

    Holger Fröning

  22. Heidelberg University, Heidelberg, Germany

    Günther Schindler

  23. University of Pisa, Pisa, Italy

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  24. University of Pisa, Pisa, Italy

    Anna Monreale

  25. ISTI-CNR, Pisa, Italy

    Salvatore Rinzivillo

  26. Warsaw University of Technology, Warsaw, Poland

    Przemyslaw Biecek

  27. Freie Universität Berlin, Berlin, Germany

    Eirini Ntoutsi

  28. Eindhoven University of Technology, Eindhoven, The Netherlands

    Mykola Pechenizkiy

  29. Leibniz University Hannover, Hannover, Germany

    Bodo Rosenhahn

  30. University of Sussex, Brighton, UK

    Christopher Buckley

  31. University of Chieti-Pescara, Chieti, Italy

    Daniela Cialfi

  32. Radboud University Nijmegen, Nijmegen, The Netherlands

    Pablo Lanillos

  33. McGill University, Montreal, Canada

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  34. Ghent University, Ghent, Belgium

    Tim Verbelen

  35. University of Lisbon, Lisboa, Portugal

    Pedro M. Ferreira

  36. University of Bari Aldo Moro, Bari, Italy

    Giuseppina Andresini

  37. Universita di Bari Aldo Moro, Bari, Italy

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  38. University of Lisbon, Lisbon, Portugal

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  39. Shenzhen University, Shenzhen, China

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  40. Harbin Institute of Technology, Harbin, China

    M. Saqib Nawaz

  41. University of Córdoba, Córdoba, Spain

    Sebastian Ventura

  42. Peking University, Beijing, China

    Meng Sun

  43. Noah's Ark Lab, Huawei, Beijing, China

    Min Zhou

  44. UniCredit, Milan, Italy

    Valerio Bitetta

  45. UniCredit, Rome, Italy

    Ilaria Bordino

  46. UniCredit, Milan, Italy

    Andrea Ferretti

  47. Unicredit, Rome, Italy

    Francesco Gullo

  48. ENEA Headquarters, Portici, Italy

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  49. Unicredit, Rome, Italy

    Lorenzo Severini

  50. University of Porto, Porto, Portugal

    Rita Ribeiro

  51. University of Porto, Porto, Portugal

    João Gama

  52. UPC BarcelonaTech, Barcelona, Spain

    Ricard Gavaldà

  53. Northwestern University, Chicago, IL, USA

    Lee Cooper

  54. PD Personalised Healthcare, Basel, Switzerland

    Naghmeh Ghazaleh

  55. University of Lausanne, Lausanne, Switzerland

    Jonas Richiardi

  56. ETH Zurich, Basel, Switzerland

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  57. F. Hoffmann–La Roche Ltd, Basel, Switzerland

    Diego Saldana Miranda

  58. Novartis Pharma AG, Basel, Switzerland

    Konstantinos Sechidis

  59. University of Lisbon, Lisbon, Portugal

    Guilherme Graça

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Tavara, S., Schliep, A., Basu, D. (2021). Federated Learning of Oligonucleotide Drug Molecule Thermodynamics with Differentially Private ADMM-Based SVM. In: Kamp, M., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1525. Springer, Cham. https://doi.org/10.1007/978-3-030-93733-1_34

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  • DOI: https://doi.org/10.1007/978-3-030-93733-1_34

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