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A roadmap to multifactor dimensionality reduction methods
- Source :
- Briefings in Bioinformatics
- Publication Year :
- 2015
-
Abstract
- Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.
- Subjects :
- 0301 basic medicine
epistasis
Multifactor Dimensionality Reduction
Computer science
interaction
Machine learning
computer.software_genre
Pattern Recognition, Automated
03 medical and health sciences
Protein Interaction Mapping
Computer Simulation
Molecular Biology
Implementation
Models, Statistical
Multifactor dimensionality reduction
business.industry
Genetic data
Regression analysis
data mining
Popularity
030104 developmental biology
machine learning
Papers
Artificial intelligence
Data mining
business
computer
Algorithms
Information Systems
Subjects
Details
- ISSN :
- 14774054
- Volume :
- 17
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Briefings in bioinformatics
- Accession number :
- edsair.doi.dedup.....9013b22c48958cf9bf345e57f3025153