MOTIVATION: Epistasis or gene-gene interaction has gained increasing attention in studies of complex diseases. Its presence as an ubiquitous component of genetic architecture of common human diseases has been contemplated. However, the detection of gene-gene interaction is difficult due to combinatorial explosion. RESULTS: We present a novel feature selection method incorporating variable interaction. Three gene expression datasets are analyzed to illustrate our method, although it can also be applied to other types of high-dimensional data. The quality of variables selected is evaluated in two ways: first by classification error rates, then by functional relevance assessed using biological knowledge. We show that the classification error rates can be significantly reduced by considering interactions. Secondly, a sizable portion of genes identified by our method for breast cancer metastasis overlaps with those reported in gene-to-system breast cancer (G2SBC) database as disease associated and some of them have interesting biological implication. In summary, interaction-based methods may lead to substantial gain in biological insights as well as more accurate prediction.
MOTIVATION: Epistasis or gene-gene interaction has gained increasing attention in studies of complex diseases. Its presence as an ubiquitous component of genetic architecture of common human diseases has been contemplated. However, the detection of gene-gene interaction is difficult due to combinatorial explosion. RESULTS: We present a novel feature selection method incorporating variable interaction. Three gene expression datasets are analyzed to illustrate our method, although it can also be applied to other types of high-dimensional data. The quality of variables selected is evaluated in two ways: first by classification error rates, then by functional relevance assessed using biological knowledge. We show that the classification error rates can be significantly reduced by considering interactions. Secondly, a sizable portion of genes identified by our method for breast cancer metastasis overlaps with those reported in gene-to-system breast cancer (G2SBC) database as disease associated and some of them have interesting biological implication. In summary, interaction-based methods may lead to substantial gain in biological insights as well as more accurate prediction.
Authors: Laura J van 't Veer; Hongyue Dai; Marc J van de Vijver; Yudong D He; Augustinus A M Hart; Mao Mao; Hans L Peterse; Karin van der Kooy; Matthew J Marton; Anke T Witteveen; George J Schreiber; Ron M Kerkhoven; Chris Roberts; Peter S Linsley; René Bernards; Stephen H Friend Journal: Nature Date: 2002-01-31 Impact factor: 49.962
Authors: C M Perou; T Sørlie; M B Eisen; M van de Rijn; S S Jeffrey; C A Rees; J R Pollack; D T Ross; H Johnsen; L A Akslen; O Fluge; A Pergamenschikov; C Williams; S X Zhu; P E Lønning; A L Børresen-Dale; P O Brown; D Botstein Journal: Nature Date: 2000-08-17 Impact factor: 49.962
Authors: Lidija Beketic-Oreskovic; Petar Ozretic; Zahid N Rabbani; Isabel L Jackson; Bozena Sarcevic; Sonja Levanat; Petra Maric; Ivan Babic; Zeljko Vujaskovic Journal: Pathol Oncol Res Date: 2011-01-20 Impact factor: 3.201
Authors: Maggie Haitian Wang; Billy Chang; Rui Sun; Inchi Hu; Xiaoxuan Xia; William Ka Kei Wu; Ka Chun Chong; Benny Chung-Ying Zee Journal: Hum Mutat Date: 2017-06-13 Impact factor: 4.878