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Daniel Scanfeld

Researcher at Columbia University Medical Center

Publications -  9
Citations -  1502

Daniel Scanfeld is an academic researcher from Columbia University Medical Center. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 8, co-authored 9 publications receiving 1394 citations. Previous affiliations of Daniel Scanfeld include Columbia University & Massachusetts Institute of Technology.

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Dissemination of health information through social networks: Twitter and antibiotics

TL;DR: Social media sites offer means of health information sharing and may provide a venue to identify misuse or misunderstanding of antibiotics, promote positive behavior change, disseminate valid information, and explore how such tools can be used to gather real-time health data.
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Distinct physiological states of Plasmodium falciparum in malaria-infected patients

TL;DR: A large study of in vivo expression profiles of parasites derived directly from blood samples from infected patients reveals a previously unknown physiological diversity in the in vivo biology of the malaria parasite, and indicates in vivo and in vitro studies to determine how this variation may affect disease manifestations and treatment.
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High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates.

TL;DR: An alternative approach that first separates the HER2+ tumors using a gene amplification signal for Her2/neu amplicon genes and then applies consensus ensemble clustering separately to the Her2+ and HER2- clusters to look for further substructure is proposed.
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Metagene projection for cross-platform, cross-species characterization of global transcriptional states

TL;DR: It is shown how a metagene projection methodology can greatly reduce the number of features used to characterize microarray data, and how this approach can help assess and interpret similarities and differences between independent data sets, enable cross-platform and cross-species analysis, improve clustering and class prediction, and provide a computational means to detect and remove sample contamination.