@inproceedings{
    Suvitaival11nipspm,
    author = "Suvitaival, Tommi and Huopaniemi, Ilkka and Ore{\v{s}}i{\v{c}}, Matej and Kaski, Samuel",
    responsibleauthor = "Kaski, Samuel",
    language = "eng",
    title = "Detecting similar high-dimensional responses to experimental factors between human and model organism",
    booktitle = {{NIPS} 2011 workshop "From Statistical Genetics to Predictive Models in Personalized Medicine"},
    corerank = "NA",
    pdf = "suvitaival11nipspm.pdf",
    note = "Extended abstract",
    flags = "AIRC HIIT public copy",
    il = "no",
    year = "to appear",
    unitcode = "T3060=99,U9014=1",
    impactfactor = "D3",
    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 matching. 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 information about the affecting factors such as gender, age, treatment, or disease status."
}