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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.

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Journal ArticleDOI

DAZL mediates a broad translational program regulating expansion and differentiation of spermatogonial progenitors.

TL;DR: In total, DAZL orchestrates a broad translational program that amplifies protein levels of key spermatogonial and gene regulatory factors to promote the expansion and differentiation of progenitor sperMatogonia.
Journal ArticleDOI

Data-driven phenotype discovery of FMR1 premutation carriers in a population-based sample.

TL;DR: The first population-based FMR1-informed biobank is created to find the pattern of health characteristics in premutation carriers and extensive phenotyping shows that premutations carriers experience a clinical profile that is significantly different from controls and is evident throughout adulthood.
Proceedings Article

Multiplicative Forests for Continuous-Time Processes

TL;DR: This work develops a partition-based representation using regression trees and forests whose parameter spaces grow linearly in the number of node splits, and shows how to update the forest likelihood in closed form, producing efficient model updates.
Journal ArticleDOI

The pituitary-testicular axis in Klinefelter's syndrome and in oligo-azoospermic patients with and without deletions of the Y chromosome long arm.

TL;DR: The function of the pituitary–testicular axis is compared in patients with severe oligospermia or azoOSpermia, idiopathic or associated with Y chromosome deletions or Klinefelter's syndrome and in control subjects.
Proceedings Article

Identifying adverse drug events by relational learning

TL;DR: This paper casts the problem of post-marketing surveillance of drugs to identify previously-unanticipated ADEs as a reverse machine learning task, related to relational subgroup discovery and provides an initial evaluation of this approach based on experiments with an actual EMR/EHR and known adverse drug events.