Open AccessFuzzy Neural Network Applied to Gene Expression Profiling for Predicting the Prognosis of Diffuse Large B‐cell Lymphoma
Author(s) -
Ando Tatsuya,
Suguro Miyuki,
Hanai Taizo,
Kobayashi Takeshi,
Honda Hiroyuki,
Seto Masao
Publication year - 2002
Publication title -
japanese journal of cancer research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.035
H-Index - 141
eISSN - 1349-7006
pISSN - 0910-5050
DOI - 10.1111/j.1349-7006.2002.tb01225.x
Subject(s) - gene expression profiling , lymphoma , malignancy , diffuse large b cell lymphoma , microarray , microarray analysis techniques , profiling (computer programming) , computational biology , oncology , biology , gene , bioinformatics , pathology , medicine , gene expression , computer science , genetics , operating system
Diffuse large B‐cell lymphoma (DLBCL) is the largest category of aggressive lymphomas. Less than 50% of patients can be cured by combination chemotherapy. Microarray technologies have recently shown that the response to chemotherapy reflects the molecular heterogeneity in DLBCL. On the basis of published microarray data, we attempted to develop a long‐overdue method for the precise and simple prediction of survival of DLBCL patients. We developed a fuzzy neural network (FNN) model to analyze gene expression profiling data for DLBCL. From data on 5857 genes, this model identified four genes ( CD10, AA807551, AA805611 and IRF‐4 ) that could be used to predict prognosis with 93% accuracy. FNNs are powerful tools for extracting significant biological markers affecting prognosis, and are applicable to various kinds of expression profiling data for any malignancy.