NDLI: Gene selection and cancer classification using a fuzzy neural network

Content Provider	IEEE Xplore Digital Library
Author	Feng Chu Wei Xie Lipo Wang
Copyright Year	2004
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore (Feng Chu; Wei Xie; Lipo Wang)
Abstract	Cancer classification based on microarray gene expressions is an important problem. In this work, we use a t- test-based feature selection method to choose some important genes from thousands of genes. After that, we classify the microarray data sets with a fuzzy neural network (FNN) that we proposed earlier. This FNN combines important features of initial fuzzy model self-generation, parameter optimization, and rule-base simplification. We applied this FNN to three well-known gene expression data sets, i.e., the lymphoma data set (with 3 sub-types), small round blue cell tumor (SRBCT) data set (with 4 sub-types), and the liver cancer data set (with 2 classes, i.e., non-tumor and hepatocellular carcinoma (HCC)). Our results in all the three data sets show that the FNN can obtain 100% accuracy with a much smaller number of genes in comparison with previously published methods. In view of the smaller number of genes required by the FNN and its high accuracy, we conclude that the FNN classifier not only helps biological researchers differentiate cancers that are difficult to be classified using traditional clinical methods, but also helps biological researchers focus on a small number of important genes to find the relationships between those important genes and the development of cancers.
Sponsorship	IEEE IEEE Syst., Man, and Cybernetics Soc. Univ. of Alberta, Faculty of Eng., Dept. of Elec. & Comput. Eng. Alberta Innovation & Sci. ASERC iCORE Alberta Ingenuity (AI) Fund Nat. Res. Council (NRC) Pearson Educ. Canada J. Wiley McGraw Hill Elsevier BV Inst. of Phys. Res. Studies Press SUN Microsystems Springer-Verlag Login Brothers Canada
Starting Page	555
Ending Page	559
File Size	326950
Page Count	5
File Format	PDF
ISBN	0780383761
DOI	10.1109/NAFIPS.2004.1337361
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-27
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cancer Fuzzy neural networks Gene expression Support vector machines Support vector machine classification Testing Neural networks Liver neoplasms Machine learning Statistical analysis
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Accurate Cancer Classification Using Expressions of Very Few Genes

Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions

A New Score Correlation Analysis Multi-class Support Vector Machine for Microarray

Cancer classification by sparse representation using microarray gene expression data

Microarray Gene Expression Cancer Diagnosis Using Multiclass Support Vector Machines

Fuzzy neural network classification design using support vector machine

Gene Selection and Classification Using Non-linear Kernel Support Vector Machines Based on Gene Expression Data

Supervised learning of maternal cigarette-smoking signatures from placental gene expression data: A case study

Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis

Gene selection and cancer classification using a fuzzy neural network

Similar Documents

Accurate Cancer Classification Using Expressions of Very Few Genes

Applying RBF Neural Networks to Cancer Classification Based on Gene Expressions

A New Score Correlation Analysis Multi-class Support Vector Machine for Microarray

Cancer classification by sparse representation using microarray gene expression data

Microarray Gene Expression Cancer Diagnosis Using Multiclass Support Vector Machines

Fuzzy neural network classification design using support vector machine

Gene Selection and Classification Using Non-linear Kernel Support Vector Machines Based on Gene Expression Data

Supervised learning of maternal cigarette-smoking signatures from placental gene expression data: A case study

Multicategory Classification Using An Extreme Learning Machine for Microarray Gene Expression Cancer Diagnosis

Gene selection and cancer classification using a fuzzy neural network