Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clustering.

ABSTRACT:

Background

The four heterogeneous childhood cancers, neuroblastoma, non-Hodgkin lymphoma, rhabdomyosarcoma, and Ewing sarcoma present a similar histology of small round blue cell tumor (SRBCT) and thus often leads to misdiagnosis. Identification of biomarkers for distinguishing these cancers is a well studied problem. Existing methods typically evaluate each gene separately and do not take into account the nonlinear interaction between genes and the tools that are used to design the diagnostic prediction system. Consequently, more genes are usually identified as necessary for prediction. We propose a general scheme for finding a small set of biomarkers to design a diagnostic system for accurate classification of the cancer subgroups. We use multilayer networks with online gene sele

SUBMITTER: Pal NR 

PROVIDER: S-EPMC1770936 | biostudies-literature | 2007 Jan

REPOSITORIES: biostudies-literature