Cross-species translation of multi-way biomarkers

Tommi Suvitaival, Ilkka Huopaniemi, Matej Oresic, Samuel Kaski

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)


We present a Bayesian translational model for matching patterns in data sets which have neither co-occurring samples nor variables, but only a similar experiment design dividing the samples into two or more categories. The model estimates covariate effects related to this design and separates the factors that are shared across the data sets from those specific to one data set. The model is designed to find similarities in medical studies, where there is great need for methods for linking laboratory experiments with model organisms to studies of human diseases and new treatments.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2011
Number of pages8
ISBN (Electronic)978-3-642-21735-7
ISBN (Print)978-3-642-21734-0
Publication statusPublished - 2011
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Artificial Neural Networks, ICANN 2011 - Espoo, Finland
Duration: 14 Jun 201117 Jun 2011

Publication series

SeriesLecture Notes in Computer Science


ConferenceInternational Conference on Artificial Neural Networks, ICANN 2011
Abbreviated titleICANN 2011


  • Bayesian inference
  • cross-species modeling
  • multi-way modeling
  • translational modeling

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