Identifying therapeutic targets by combining transcriptional data with ordinal clinical measurements

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...

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Bibliographic Details
Main Authors: Dunn, Denise E. (Author), Avila-Pacheco, Julian (Author), Greengard, Paul (Author), Clish, Clary B. (Author), Lo, Donald C. (Author), Pirhaji, Leila (Contributor), Milani, Pamela (Contributor), Dalin, Simona (Contributor), Wassie, Brook T. (Contributor), Fenster, Robert (Contributor), Heiman, Myriam (Contributor), Fraenkel, Ernest (Contributor)
Other Authors: Massachusetts Institute of Technology. Department of Biological Engineering (Contributor), Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences (Contributor), Picower Institute for Learning and Memory (Contributor)
Format: Article
Language:English
Published: Nature Publishing Group, 2017-11-14T19:12:48Z.
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