title: Non-linear cancer classification using a modified radial basis function classification algorithm.
abstract: This paper proposes a modified radial basis function classification algorithm for non-linear cancer classification. In the algorithm, a modified simulated annealing method is developed and combined with the linear least square and gradient paradigms to optimize the structure of the radial basis function (RBF) classifier. The proposed algorithm can be adopted to perform non-linear cancer classification based on gene expression profiles and applied to two microarray data sets involving various human tumor classes: (1) Normal versus colon tumor; (2) acute myeloid leukemia (AML) versus acute lymphoblastic leukemia (ALL). Finally, accuracy and stability for the proposed algorithm are further demonstrated by comparing with the other cancer classification algorithms.
authors.affiliation: Intelligent Computation Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Science, P.O. Box 1130, , Hefei, Anhui, 230031, China. hqwang@iim.ac.cn
authors.forename: Hong-Qiang
authors.initials: HQ
authors.lastname: Wang
authors.forename: De-Shuang
authors.initials: DS
authors.lastname: Huang
mesh_terms: D000208:Acute Disease; D000465:Algorithms; D003110:Colonic Neoplasms / Q000145:classification*; D006801:Humans; D007951:Leukemia, Myeloid / Q000145:classification*; D054198:Precursor Cell Lymphoblastic Leukemia-Lymphoma / Q000145:classification*