Matching Models Across Abstraction Levels with Gaussian Processes

Giulio Caravagna, Luca Bortolussi, Guido Sanguinetti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Biological systems are often modelled at different levels of abstraction depending on the particular aims/resources of a study. Such different models often provide qualitatively concordant predictions over specific parametrisations, but it is generally unclear whether model predictions are quantitatively in agreement, and whether such agreement holds for different parametrisations. Here we present a generally applicable statistical machine learning methodology to automatically reconcile the predictions of different models across abstraction levels. Our approach is based on defining a correction map, a random function which modifies the output of a model in order to match the statistics of the output of a different model of the same system. We use two biological examples to give a proof-of-principle demonstration of the methodology, and discuss its advantages and potential further applications.
Original languageEnglish
Title of host publicationComputational Methods in Systems Biology
Subtitle of host publication14th International Conference, CMSB 2016, Cambridge, UK, September 21-23, 2016, Proceedings
EditorsEzio Bartocci, Pietro Lio, Nicola Paoletti
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages18
ISBN (Electronic)978-3-319-45177-0
ISBN (Print)978-3-319-45176-3
Publication statusPublished - 4 Sept 2016
Event14th International Conference on Computational Methods in Systems Biology - Cambridge, United Kingdom
Duration: 21 Sept 201623 Sept 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743


Conference14th International Conference on Computational Methods in Systems Biology
Abbreviated titleCMSB 2016
Country/TerritoryUnited Kingdom
Internet address


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