Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell Lymphoma

Tatsuya Ando, Miyuki Suguro, Taizo Hanai, Takeshi Kobayashi, Hiroyuki Honda, Masao Seto

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)1207-1212
Number of pages6
JournalJapanese Journal of Cancer Research
Volume93
Issue number11
DOIs
Publication statusPublished - Nov 1 2002

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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