Fuzzy Neural Network Applied to Gene Expression Profiling for Predicting the Prognosis of Diffuse Large B‐cell Lymphoma

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

doi:10.1111/j.1349-7006.2002.tb01225.x

Cited by 47 publications

(26 citation statements)

References 24 publications

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“…11,16) Optimal number of gene combinations. In the previous study, 13) we constructed an FNN model consisting of 4 genes (CD10, AA807551, AA805611 and IRF-4) for outcome prediction of 40 DLBCL patients, and this model predicted prognosis with 93% accuracy. For the 58 DLBCL patient data reported by Shipp et al, 12) we constructed FNN models with combinations of 10 genes.…”

Section: Resultsmentioning

confidence: 99%

“…[11][12][13][14] Previously, we used a fuzzy neural network (FNN) model to identify several genes with which we constructed noninferior prognostic models of DLBCL, and we achieved accuracy greater than 90% using these models. 13,14) The FNN model is a relatively advanced artificial neural network (ANN) model. The FNN model has the advantages of extremely high prognostic accuracy and the ability to explicitly describe causality between input and output variables as linguistic IF-THEN rules.…”

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Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling

et al. 2003

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A fuzzy neural network (FNN) using gene expression profile data can select combinations of genes from thousands of genes, and is applicable to predict outcome for cancer patients after chemotherapy. However, wide clinical heterogeneity reduces the accuracy of prediction. To overcome this problem, we have proposed an FNN system based on majoritarian decision using multiple noninferior models. We used transcriptional profiling data, which were obtained from "Lymphochip" DNA microarrays (http:// llmpp.nih.gov/DLBCL), reported by Rosenwald (N Engl J Med 2002; 346: 1937-47). When the data were analyzed by our FNN system, accuracy (73.4%) of outcome prediction using only 1 FNN model with 4 genes was higher than that (68.5%) of the Cox model using 17 genes. Higher accuracy (91%) was obtained when an FNN system with 9 noninferior models, consisting of 35 independent genes, was used. The genes selected by the system included genes that are informative in the prognosis of Diffuse large B-cell lymphoma (DLBCL), such as genes showing an expression pattern similar to that of CD10 and BCL-6 or similar to that of IRF-4 and BCL-4. We classified 220 DLBCL patients into 5 groups using the prediction results of 9 FNN models. These groups may correspond to DLBCL subtypes. In group A containing half of the 220 patients, patients with poor outcome were found to satisfy 2 rules, i.e., high expression of MAX dimerization with high expression of unknown A (LC_26146), or high expression of MAX dimerization with low expression of unknown B (LC_33144). The present paper is the first to describe the multiple noninferior FNN modeling system. This system is a powerful tool for predicting outcome and classifying patients, and is applicable to other heterogeneous diseases. (Cancer Sci 2003; 94: 906-913) iffuse large B-cell lymphoma (DLBCL) is the largest category of lymphoid malignancy, accounting for 30 to 40% of non-Hodgkin's lymphomas. 1) From 35 to 40% of DLBCL patients can be cured by current chemotherapeutic regimens; the remaining 60 to 65% eventually succumb to this disease. Marked clinical heterogeneity of DLBCL has been reported by many researchers.2-5) Some patients are not correctly diagnosed, and diagnostic categories have not been defined molecularly. [6][7][8][9][10] Molecular analyses of clinical heterogeneity in DLBCL have been conducted using microarray technologies. Various computational analyses using small numbers of DLBCL patients have been presented. [11][12][13][14] Previously, we used a fuzzy neural network (FNN) model to identify several genes with which we constructed noninferior prognostic models of DLBCL, and we achieved accuracy greater than 90% using these models. 13,14) The FNN model is a relatively advanced artificial neural network (ANN) model. The FNN model has the advantages of extremely high prognostic accuracy and the ability to explicitly describe causality between input and output variables as linguistic IF-THEN rules.15) We extracted prognostic rules based on the expression profiles of only 4 genes. I...

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Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling

et al. 2003

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“…Gene expression levels can be measured using microarray technology 36 . Gene expression levels are often used to compare regular and cancerous cells, e.g., the authors of 34,3,62 have been using NN successfully on classification problems in the context of cancerous cells. Gene expression data is also used to create gene regulatory networks; these are maps that describe how genes influence each other's expression levels.…”

Section: Gene Expression Analysis and Gene Regulatory Networkmentioning

confidence: 99%

An overview of the use of neural networks for data mining tasks

Stahl

Jordanov

2012

WIREs Data Min & Knowl

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In the recent years, the area of data mining has been experiencing considerable demand for technologies that extract knowledge from large and complex data sources. There has been substantial commercial interest as well as active research in the area that aim to develop new and improved approaches for extracting information, relationships, and patterns from large datasets. Artificial neural networks (NNs) are popular biologically-inspired intelligent methodologies, whose classification, prediction, and pattern recognition capabilities have been utilized successfully in many areas, including science, engineering, medicine, business, banking, telecommunication, and many other fields. This paper highlights from a data mining perspective the implementation of NN, using supervised and unsupervised learning, for pattern recognition, classification, prediction, and cluster analysis, and focuses the discussion on their usage in bioinformatics and financial data analysis tasks.

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“…There is growing interest in the application of ANNs in microarray analysis [17][18][19][20][21]. The majority of these papers conclude unique and superior classification results obtained with ANNs when compared with other classifiers.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Analysis of DNA Microarray-based Prostate Cancer Recurrence

Peterson

Özen

Erdem

et al. 2005

2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology

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-DNA microarray-based gene expression profiles have been established for a variety of adult cancers. This paper addresses application of an artificial neural network (ANN) with leave-oneout testsing and 8-fold cross-validation for analyzing DNA microarray data to identify genes predictive of recurrence after prostatectomy. Among 725 genes screened for ANN input, a 16-gene model resulted in 99-100% diagnostic sensitivity and specificity: DGCR5,

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Fuzzy Neural Network Applied to Gene Expression Profiling for Predicting the Prognosis of Diffuse Large B‐cell Lymphoma

Cited by 47 publications

References 24 publications

Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling

Multiple fuzzy neural network system for outcome prediction and classification of 220 lymphoma patients on the basis of molecular profiling

An overview of the use of neural networks for data mining tasks

Artificial Neural Network Analysis of DNA Microarray-based Prostate Cancer Recurrence

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