skip to main content

Explore Research Products in the NSF-PAR

  • Home
  • Search Results
  • Page 1 of 1

Search for: All records

Creators/Authors contains: "Yang, Dana"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; ; ( , Probability Theory and Related Fields)
    Free, publicly-accessible full text available June 6, 2024
  2. ; ; ( , IEEE Transactions on Information Theory)
    Full Text Available 
  3. ; ; ; ( , IEEE Transactions on Information Theory)
    null (Ed.)
    Full Text Available 
  4. ; ; ( , International Conference on Machine Learning)
    null (Ed.)
    Accepted Manuscript Full Text Available
  5. ; ; ( , International Conference on Artificial Intelligence and Statistics)
    null (Ed.)
    Accepted Manuscript Full Text Available
  6. ; ( , Proceedings of Machine Learning Research)
    null (Ed.)
    Accepted Manuscript Full Text Available
  7. ; ; ; ( , 33rd Annual Conference on Learning Theory)
    null (Ed.)
    Accepted Manuscript Full Text Available
  8. ; ; ; ( , Proceedings of Thirty Third Conference on Learning Theory)
    null (Ed.)
    Accepted Manuscript Full Text Available
  9. ; ; ; ( , 33rd Annual Conference on Learning Theory)
    Accepted Manuscript Full Text Available
  10. ( , RECOMB Satellite on Genetics)
    Motivation: Modeling genetics of gene expression had been effective at highlighting cis-eQTLs, variants that control nearby transcripts. Yet, incorporation of long-range effects has been hampered by unfavora- ble statistical considerations. On the other end, expression alone has been modeled across tissues by decomposition into contributing factors, without any connection to genetics. Results: We develop MIxed-Layer Analysis of Genetics and Expression (MILAGE), a model that combines direct effects of cis-SNPs on nearby transcripts with trans-effects that control global factors of expression in a tissue-specific pattern. We develop judicious initialization of the model, followed by gradient descent learning. We present GPU-based implementation of the learner to enable computational feasibility in this otherwise intractably-large parameter space. We show the model to explain > 59% of test-set variation in GTEx data. The inferred genetically-regulated factors are consistent with expected tissue similarity. 
    more » « less
    Accepted Manuscript Full Text Available