@inproceedings{b6300ab4d8b546839b15e250840d784b,
title = "Cross-species translation of multi-way biomarkers",
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.",
keywords = "Bayesian inference, cross-species modeling, multi-way modeling, translational modeling",
author = "Tommi Suvitaival and Ilkka Huopaniemi and Matej Ore{\v s}i{\v c} and Samuel Kaski",
year = "2011",
month = jun,
day = "24",
doi = "10.1007/978-3-642-21735-7\_26",
language = "English",
isbn = "9783642217340",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
number = "PART 1",
pages = "209--216",
booktitle = "Artificial Neural Networks and Machine Learning, ICANN 2011 - 21st International Conference on Artificial Neural Networks, Proceedings",
edition = "PART 1",
note = "21st International Conference on Artificial Neural Networks, ICANN 2011 ; Conference date: 14-06-2011 Through 17-06-2011",
}