Medicine and Health Sciences Commons^™

Subject Level Clustering Using A Negative Binomial Model For Small Transcriptomic Studies., Qian Li, Janelle R. Noel-Macdonnell, Devin C. Koestler, Ellen L. Goode, Brooke L. Fridley

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: Unsupervised clustering represents one of the most widely applied methods in analysis of high-throughput 'omics data. A variety of unsupervised model-based or parametric clustering methods and non-parametric clustering methods have been proposed for RNA-seq count data, most of which perform well for large samples, e.g. N ≥ 500. A common issue when analyzing limited samples of RNA-seq count data is that the data follows an over-dispersed distribution, and thus a Negative Binomial likelihood model is often used. Thus, we have developed a Negative Binomial model-based (NBMB) clustering approach for application to RNA-seq studies.

RESULTS: We have developed a Negative …

User-Centered Design Of Multi-Gene Sequencing Panel Reports For Clinicians., Elizabeth Cutting, Meghan Banchero, Amber L. Beitelshees, James J. Cimino, Guilherme Del Fiol, Ayse P. Gurses, Mark A. Hoffman, Linda Jo Bone Jeng, Kensaku Kawamoto, Mark Kelemen, Harold Alan Pincus, Alan R. Shuldiner, Marc S. Williams, Toni I. Pollin, Casey Lynnette Overby

Manuscripts, Articles, Book Chapters and Other Papers

The objective of this study was to develop a high-fidelity prototype for delivering multi-gene sequencing panel (GS) reports to clinicians that simulates the user experience of a final application. The delivery and use of GS reports can occur within complex and high-paced healthcare environments. We employ a user-centered software design approach in a focus group setting in order to facilitate gathering rich contextual information from a diverse group of stakeholders potentially impacted by the delivery of GS reports relevant to two precision medicine programs at the University of Maryland Medical Center. Responses from focus group sessions were transcribed, coded and …

A Comparison Of A Multistate Inpatient Ehr Database To The Hcup Nationwide Inpatient Sample., Jonathan P Deshazo, Mark A Hoffman

Manuscripts, Articles, Book Chapters and Other Papers

BACKGROUND: The growing availability of electronic health records (EHRs) in the US could provide researchers with a more detailed and clinically relevant alternative to using claims-based data.

METHODS: In this study we compared a very large EHR database (Health Facts©) to a well-established population estimate (Nationwide Inpatient Sample). Weighted comparisons were made using t-value and relative difference over diagnoses and procedures for the year 2010.

RESULTS: The two databases have a similar distribution pattern across all data elements, with 24 of 50 data elements being statistically similar between the two data sources. In general, differences that were found are consistent …