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David C. Page
Researcher at Massachusetts Institute of Technology
Publications - 523
Citations - 47344
David C. Page is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Y chromosome & X chromosome. The author has an hindex of 110, co-authored 509 publications receiving 44119 citations. Previous affiliations of David C. Page include Hennepin County Medical Center & University of California, Los Angeles.
Papers
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Journal ArticleDOI
Methodological Considerations for Comparison of Brand Versus Generic Versus Authorized Generic Adverse Event Reports in the US Food and Drug Administration Adverse Event Reporting System (FAERS).
Motiur Rahman,Yasser Alatawi,Ning Cheng,Jingjing Qian,Peggy L. Peissig,Richard L. Berg,David C. Page,Richard A. Hansen +7 more
TL;DR: Differentiation of FAERS reports as brand versus generic requires careful attention to risk of product misclassification, but the relative stability of findings across varying assumptions supports the utility of these approaches for potential signal detection.
Journal ArticleDOI
Tumor cell sensitivity to vemurafenib can be predicted from protein expression in a BRAF -V600E basket trial setting
TL;DR: The results support that inclusion of proteomics can predict drug response and identify co-therapies in a basket setting and predicted vemurafenib sensitivity with greater accuracy in both melanoma and non-melanoma BRAF-V600E cell lines than other leading machine learning methods.
Posted ContentDOI
Conserved microRNA targeting reveals preexisting gene dosage sensitivities that shaped amniote sex chromosome evolution
TL;DR: A more complete view of the role of dosage sensitivity in shaping the mammalian and avian sex chromosomes is provided, and an important role for post-transcriptional regulatory sequences (miRNA target sites) in sex chromosome evolution is revealed.
Proceedings Article
AUCμ: A Performance Metric for Multi-Class Machine Learning Models
Ross Kleiman,David C. Page +1 more
TL;DR: This work provides in this work a multi-class extension of AUC that is called AUCμ that is derived from first principles of the binary class AUC, which has similar computational complexity to AUC and maintains the properties of A UC critical to its interpretation and use.
Posted Content
Privacy-Preserving Collaborative Prediction using Random Forests
TL;DR: This work proposes a new approach for ensemble methods: each entity learns a model, from its own data, and then when a client asks the prediction for a new private instance, the answers from all the locally trained models are used to compute the prediction in such a way that no extra information is revealed.