Detecting similar high-dimensional responses to experimental factors between human and model organism
Tommi Suvitaival, Ilkka Huopaniemi, Matej Oresic and Samuel Kaski
In: NIPS 2011 workshop From Statistical Genetics to Predictive Models in Personalized Medicine(2011).

Abstract

We present a Bayesian model for analysing the effect of multiple experimental factors in two-species studies without the requirement of a priori known match- ing. From model studies of human diseases, conducted using *omics technologies and various model organisms, the question emerges: is there something similar in the molecular responses of the different organisms under certain conditions, such as healthy vs. diseased? Our approach provides a generative model for the task of analysing multi-species data, naturally taking into account the additional in- formation about the affecting factors such as gender, age, treatment, or disease status.

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