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Creators/Authors contains: "Yang, Dana"

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  1. ; ; ; (, Probability Theory and Related Fields)
    Full Text Available 
  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)
    Accepted Manuscript Full Text Available
  8. ; ; ; (, 33rd Annual Conference on Learning Theory)
    null (Ed.)
    Accepted Manuscript Full Text Available
  9. ; ; ; (, Proceedings of Thirty Third Conference on Learning Theory)
    null (Ed.)
    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. 
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    Accepted Manuscript Full Text Available