title: Tri-mean-based statistical differential gene expression detection.
abstract: Based on the assumption that only a subset of disease group has differential gene expression, traditional detection of differentially expressed genes is under the constraint that cancer genes are up- or down-regulated in all disease samples compared with normal samples. However, in 2005, Tomlins assumed and discussed the situation that only a subset of disease samples would be activated, which are often referred to as outliers.
authors.affiliation: Key Laboratory of Symbolic Computation and Knowledge Engineering, College of Computer Science and Technology, Jilin University, 130012 Changchun, China. jzh978@163.com
authors.forename: Zhaohua
authors.initials: Z
authors.lastname: Ji
authors.forename: Chunguo
authors.initials: C
authors.lastname: Wu
authors.forename: Yao
authors.initials: Y
authors.lastname: Wang
authors.forename: Renchu
authors.initials: R
authors.lastname: Guan
authors.forename: Huawei
authors.initials: H
authors.lastname: Tu
authors.forename: Xiaozhou
authors.initials: X
authors.lastname: Wu
authors.forename: Yanchun
authors.initials: Y
authors.lastname: Liang
mesh_terms: D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D009857:Oncogenes / Q000235:genetics