A novel feature ensemble technology to improve prediction performance of multiple heterogeneous phenotypes based on microarray data

Authors:
Haiyun Wang;Qingpu Zhang;Yadong Wang;Xia Li;Shaoqi Rao;Zuquan Ding
Affiliations:
,Institute of Life Science and Medical Engineering, TongJi University, ShangHai, China;Harbin Institute of Technology, Harbin, China;Harbin Institute of Technology, Harbin, China;,Institute of Life Science and Medical Engineering, TongJi University, ShangHai, China;Departments of Cardiovascular Medicine and Molecular Cardiology, Cleveland Clinic Foundation, Cleveland, Ohio;Institute of Life Science and Medical Engineering, TongJi University, ShangHai, China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

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Abstract

Gene expression microarray technology provides the global information on transcriptional activities of essentially all genes simultaneously, and it thus promotes the new application of traditional feature selection methods in the fields of molecular biology and life sciences. The basic strategy for the traditional feature selection methods is to seek for a single gene subset that leads to the best prediction of biological types, for example tumor versus normal tissues. Because of complexities and genetic heterogeneities of biological phenotypes (e.g. complex diseases), robust computational approaches are desirable to achieve high generalization performance with multiple classifiers and perturbations of the data structures. The purpose of this study is to develop an ensemble decision approach to analysis of multiple heterogeneous phenotypes. The results from an application to a lymphoma data of five subtypes indicate that the proposed analysis strategy is feasible and powerful to perform biological subtype.