Summary: In this paper, we introduce multiple-matching Evidence-based Translator (mEBT) to discover genomic responses from murine expression data for human immune studies, which are significant in the given condition of mice and likely have similar responses in the corresponding condition of human. mEBT is evaluated over multiple datasets and shows improved inter-species agreement. mEBT is expected to be useful for research groups who use murine models to study human immunity.
Bibliographical noteFunding Information:
This work has been supported by grants from National Research Foundation of Korea (NRF-2017R1C1B2002850) and Korea Evaluation Institute of Industrial Technology (10073166).
© The Author(s) 2018. Published by Oxford University Press. All rights reserved.
ASJC Scopus subject areas
- Statistics and Probability
- Molecular Biology
- Computer Science Applications
- Computational Theory and Mathematics
- Computational Mathematics