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Murray H. Brilliant

dblp:180/9543 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 2018
0000-0002-2753-0699ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 2Artificial intelligence and machine learning · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Medical and health informatics · 50% Bioinformatics and computational biology · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Medical and health informatics › electronic health records
electronic health record analysis
0.312018
Applying family analyses to electronic health records to facilitate genetic research · Bioinform. 2018
Bioinformatics and computational biology › statistical genetics › genetic association study
genetic association testing
0.312018
Applying family analyses to electronic health records to facilitate genetic research · Bioinform. 2018

Methods — techniques the papers use, named apart from their topics

statistical method for genetic component identification · 0.3
YearPublicationVenuePosition
2018 Applying family analyses to electronic health records to facilitate genetic research
abstract
Motivation: Pedigree analysis is a longstanding and powerful approach to gain insight into the underlying genetic factors in human health, but identifying, recruiting and genotyping families can be difficult, time consuming and costly. Development of high throughput methods to identify families and foster downstream analyses are necessary. Results: This paper describes simple methods that allowed us to identify 173 368 family pedigrees with high probability using basic demographic data available in most electronic health records (EHRs). We further developed and validate a novel statistical method that uses EHR data to identify families more likely to have a major genetic component to their diseases risk. Lastly, we showed that incorporating EHR-linked family data into genetic association testing may provide added power for genetic mapping without additional recruitment or genotyping. The totality of these results suggests that EHR-linked families can enable classical genetic analyses in a high-throughput manner. Availability and implementation: Pseudocode is provided as supplementary information. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.
Xiayuan Huang, Robert C. Elston, Guilherme J. M. Rosa, John Mayer, Zhan Ye, Terrie E. Kitchner, Murray H. Brilliant, David Page, Scott J. Hebbring
Bioinform.7
2014 Benefits of Accurate Imputations in GWAS
Shefali S. Verma, Peggy L. Peissig, Deanna S. Cross, Carol Waudby, Murray H. Brilliant, Catherine A. McCarty, Marylyn D. Ritchie
EvoApplications5