Open AccessBoosting Multifactor Dimensionality Reduction Using Pre‐evaluation
Author(s) -
Hong Yingfu,
Lee Sangbum,
Oh Sejong
Publication year - 2016
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.16.0114.0040
Subject(s) - multifactor dimensionality reduction , boosting (machine learning) , dimensionality reduction , curse of dimensionality , computer science , machine learning , data mining , execution time , artificial intelligence , reduction (mathematics) , gene , parallel computing , mathematics , biology , biochemistry , geometry , genotype , single nucleotide polymorphism
The detection of gene–gene interactions during genetic studies of common human diseases is important, and the technique of multifactor dimensionality reduction (MDR) has been widely applied to this end. However, this technique is not free from the “curse of dimensionality” — that is, it works well for two‐ or three‐way interactions but requires a long execution time and extensive computing resources to detect, for example, a 10‐way interaction. Here, we propose a boosting method to reduce MDR execution time. With the use of pre‐evaluation measurements, gene sets with low levels of interaction can be removed prior to the application of MDR. Thus, the problem space is decreased and considerable time can be saved in the execution of MDR.