Sciweavers

Share
BMCBI
2007

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

11 years 1 months ago
Discovering biomarkers from gene expression data for predicting cancer subgroups using neural networks and relational fuzzy clus
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 selection ability and relational fuzzy clustering to identify a small set of biomarkers for accurate classification of the training and blind test cases of a well studied data set. Results: Our method discerned...
Nikhil R. Pal, Kripamoy Aguan, Animesh Sharma, Shu
Added 09 Dec 2010
Updated 09 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Nikhil R. Pal, Kripamoy Aguan, Animesh Sharma, Shun-ichi Amari
Comments (0)
books