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MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues
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MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues

Bioinformatics, 2016 Feb 15, Vol.32(4), pp.523-532 [Peer Reviewed Journal]

Web of Science

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MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues

Bioinformatics (Oxford, England), 15 February 2016, Vol.32(4), pp.523-32 [Peer Reviewed Journal]

MEDLINE/PubMed (U.S. National Library of Medicine)

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MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues.

Bioinformatics (Oxford, England), February 15, 2016, Vol.32(4), pp.523-532 [Peer Reviewed Journal]

© ProQuest LLC All rights reserved

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MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues

Bioinformatics, 26 October 2015 [Peer Reviewed Journal]

Spiral (Imperial College London)

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MT-HESS: An efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues

Bioinformatics, 15 February 2016, Vol.32(4), pp.523-532 [Peer Reviewed Journal]

Scopus (Elsevier B.V)

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Article
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MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues

Bioinformatics, February 15, 2016, Vol.32(4), pp.523-532 [Peer Reviewed Journal]

© ProQuest LLC All rights reserved

No full-text

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