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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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Isolating mitotic and meiotic germ cells from male mice by developmental synchronization, staging, and sorting
TL;DR: The “3S” method can separate germ cell types that were previously challenging or impossible to distinguish, with sufficient yield for epigenetic and biochemical studies, and enable detailed characterization of molecular changes that occur during the mitotic and meiotic phases of spermatogenesis.
Journal ArticleDOI
Four PCR-based polymorphisms in the pseudoautosomal region of the human X and Y chromosomes
TL;DR: Four PCR-based polymorphisms in the pseudoautosomal region of the human X and Y chromosomes Karin Schmitt, Douglas Votlrath, Simon Foote and Norman Arnheim.
Journal ArticleDOI
Supporting evidence-based analysis for modified risk tobacco products through a toxicology data-sharing infrastructure.
Stéphanie Boué,Thomas Exner,Samik Ghosh,Vincenzo Belcastro,Joh Dokler,David C. Page,Akash Boda,Filipe Bonjour,Barry Hardy,Patrick Vanscheeuwijck,Julia Hoeng,Manuel C. Peitsch +11 more
TL;DR: A proof-of-concept database and website has been developed to share results from both in vivo inhalation studies and in vitro studies conducted by Philip Morris International R&D to assess candidate MRTPs, and the goal is to establish a public repository for 21 st-century preclinical systems toxicology MRTP assessment data and results that supports open data principles.
Book ChapterDOI
Inferring Regulatory Networks from Time Series Expression Data and Relational Data Via Inductive Logic Programming
TL;DR: An application of an inductive logic programming (ILP) system to the task of identifying important regulatory relationships from discretized time series gene expression data, protein-protein interaction, protein phosphorylation and transcription factor data about the organism is described.
Proceedings ArticleDOI
Breast Cancer Risk Prediction Using Electronic Health Records
Yirong Wu,Elizabeth S. Burnside,Jennifer Cox,Jun Fan,Ming Yuan,Jie Yin,Peggy L. Peissig,Alexander G. Cobian,David C. Page,Mark Craven +9 more
TL;DR: The performance of two models to predict breast cancer one year in advance based on diagnosis codes in three levels of data representation demonstrates that EHR data can be used to predict Breast cancer risk, which provides the possibility to personalize care in clinical practice.