Cross-species translation of multi-way biomarkers

Reference:

Tommi Suvitaival, Ilkka Huopaniemi, Matej Orešic, and Samuel Kaski. Cross-species translation of multi-way biomarkers. In Timo Honkela, Wlodzislaw Duch, Mark Girolami, and Samuel Kaski, editors, Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN), Part I, volume 6791 of Lecture Notes in Computer Science, pages 209–216. Springer, 2011.

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.

Suggested BibTeX entry:

@inproceedings{Suvitaival11,
    author = {Tommi Suvitaival and Ilkka Huopaniemi and Matej Ore{\v{s}}i{\v{c}} and Samuel Kaski},
    booktitle = {Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN), Part I},
    editor = {Honkela, Timo and Duch, Wlodzislaw and Girolami, Mark and Kaski, Samuel},
    language = {eng},
    pages = {209--216},
    publisher = {Springer},
    series = {Lecture Notes in Computer Science},
    title = {Cross-species translation of multi-way biomarkers},
    volume = {6791},
    year = {2011},
}