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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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Proceedings Article
Discovering latent structure in clinical databases
Jesse Davis,Elizabeth Berg,David C. Page,Vítor Santos Costa,Peggy L. Peissig,Michael D. Caldwell +5 more
TL;DR: This work presents a novel algorithm that automatically groups together different objects in a domain in order to uncover latent structure, including a hierarchy or even heterarchy, and finds interesting latent structure that was deemed to be relevant and interesting by a medical collaborator.
Posted Content
A Simple Text Mining Approach for Ranking Pairwise Associations in Biomedical Applications
TL;DR: KinderMiner as discussed by the authors is a simple text mining method that is easy to implement, requires minimal data collection and preparation, and can be used for proposing ranked associations between a list of target terms and a key phrase.
Posted ContentDOI
Locating a transgene integration site by nanopore sequencing
TL;DR: N nanopore sequencing is applied in search of the site of integration of Tg(Pou5f1-EGFP)2Mnm (also known as Oct4:EGFP), a widely used fluorescent reporter in mouse germ line research, and it is suggested that such an approach provides a rapid, cost-effective method for identifying and analyzing transgene integration sites.
Posted Content
Learning Bayesian Network Structure from Correlation-Immune Data
TL;DR: In this article, the Sparse Candidate algorithm is extended with a technique called "skewing", which is able to discover relationships between random variables that are approximately correlation-immune, with a significantly lower computational cost than the alternative of considering multiple parents of a node at a time.