MebT: Multiple-matching evidence-based translator of murine genomic responses for human immunity studies

Donghyun Tae; Junhee Seok

doi:10.1093/bioinformatics/bty431

MebT: Multiple-matching evidence-based translator of murine genomic responses for human immunity studies

Donghyun Tae, Junhee Seok

School of Electrical Engineering

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	English
Pages (from-to)	3741-3743
Number of pages	3
Journal	Bioinformatics
Volume	34
Issue number	21
DOIs	https://doi.org/10.1093/bioinformatics/bty431
Publication status	Published - 2018 Nov 1

ASJC Scopus subject areas

Statistics and Probability
Biochemistry
Molecular Biology
Computer Science Applications
Computational Theory and Mathematics
Computational Mathematics

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10.1093/bioinformatics/bty431

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title = "MebT: Multiple-matching evidence-based translator of murine genomic responses for human immunity studies",

abstract = "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.",

author = "Donghyun Tae and Junhee Seok",

note = "Funding Information: This work has been supported by grants from National Research Foundation of Korea (NRF-2017R1C1B2002850) and Korea Evaluation Institute of Industrial Technology (10073166). Publisher Copyright: {\textcopyright} The Author(s) 2018. Published by Oxford University Press. All rights reserved.",

year = "2018",

month = nov,

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doi = "10.1093/bioinformatics/bty431",

language = "English",

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journal = "Bioinformatics",

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T2 - Multiple-matching evidence-based translator of murine genomic responses for human immunity studies

AU - Tae, Donghyun

AU - Seok, Junhee

N1 - Funding Information: This work has been supported by grants from National Research Foundation of Korea (NRF-2017R1C1B2002850) and Korea Evaluation Institute of Industrial Technology (10073166). Publisher Copyright: © The Author(s) 2018. Published by Oxford University Press. All rights reserved.

PY - 2018/11/1

Y1 - 2018/11/1

N2 - 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.

AB - 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.

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U2 - 10.1093/bioinformatics/bty431

DO - 10.1093/bioinformatics/bty431

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JO - Bioinformatics

JF - Bioinformatics

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ER -

MebT: Multiple-matching evidence-based translator of murine genomic responses for human immunity studies

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