Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements
The immense and growing repositories of transcriptional data may contain critical insights for developing new therapies. Current approaches to mining these data largely rely on binary classifications of disease vs. control, and are not able to incorporate measures of disease severity. We report an a...
Main Authors: | Dunn, Denise E., Avila-Pacheco, Julian, Greengard, Paul, Clish, Clary B., Lo, Donald C., Pirhaji, Leila, Milani, Pamela, Dalin, Simona, Wassie, Brook T., Fenster, Robert, Heiman, Myriam, Fraenkel, Ernest |
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Other Authors: | Massachusetts Institute of Technology. Department of Biological Engineering |
Format: | Article |
Published: |
Nature Publishing Group
2017
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Online Access: | http://hdl.handle.net/1721.1/112189 https://orcid.org/0000-0001-6246-276X https://orcid.org/0000-0003-0250-0474 https://orcid.org/0000-0001-5024-9718 https://orcid.org/0000-0002-6365-8673 https://orcid.org/0000-0001-9249-8181 |
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