Cross-species translation of multi-way biomarkers

Authors:
Tommi Suvitaival;Ilkka Huopaniemi;Matej Orešič;Samuel Kaski
Affiliations:
Aalto University School of Science, Department of Information and Computer Science, Helsinki Institute for Information Technology;Aalto University School of Science, Department of Information and Computer Science, Helsinki Institute for Information Technology;VTT Technical Research Centre of Finland;Aalto University School of Science, Department of Information and Computer Science, Helsinki Institute for Information Technology and University of Helsinki, Department of Computer Science
Venue:
ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
Year:
2011

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Abstract

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.