Multifactor dimensionality reduction software for detecting gene–gene and gene–environment interactions
Clicks: 3
ID: 296895
2003
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Popular Article
0.6
/100
3 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #335 of 829 articles by views in BMC Bioinformatics
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 829 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Motivation: Polymorphisms in human genes are being described in remarkable numbers. Determining which polymorphisms and which environmental factors are associated with common, complex diseases has become a daunting task. This is partly because the effect of any single genetic variation will likely be dependent on other genetic variations (gene–gene interaction or epistasis) and environmental factors (gene–environment interaction). Detecting and characterizing interactions among multiple factors is both a statistical and a computational challenge. To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for collapsing high-dimensional genetic data into a single dimension thus permitting interactions to be detected in relatively small sample sizes. In this paper, we describe the MDR approach and an MDR software package. Results: We developed a program that integrates MDR with a cross-validation strategy for estimating the classification and prediction error of multifactor models. The software can be used to analyze interactions among 2–15 genetic and/or environmental factors. The dataset may contain up to 500 total variables and a maximum of 4000 study subjects. Availability: Information on obtaining the executable code, example data, example analysis, and documentation is available upon request. Contact: moore@phg.mc.vanderbilt.edu Supplementary information: All supplementary information can be found at http://phg.mc.vanderbilt.edu/Software/MDR. * To whom correspondence should be addressed.
| Reference Key |
openalex_W2131878646
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Lance W. Hahn, Marylyn D. Ritchie, Jason H. Moore |
| Journal | BMC Bioinformatics |
| Year | 2003 |
| DOI |
10.1093/bioinformatics/btf869
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.