NDLI: Molecular Diagnosis of Tumor Based on Independent Component Analysis and Support Vector Machines

Content Provider	IEEE Xplore Digital Library
Author	Shulin Wang Huowang Chen Ji Wang Dingxing Zhang
Copyright Year	2006
Description	Author affiliation: Sch. of Comput. Sci.,, Nat. Univ. of Defense Technol., Changsha (Shulin Wang; Huowang Chen; Ji Wang; Dingxing Zhang)
Abstract	Gene expression data that is being used to gather information from tissue samples is expected to significantly improve the development of efficient tumor diagnosis. For more accurate classification of tumor, extracting discriminant components from thousands of genes is an important problem which becomes challenging task due to the large number of genes and small sample size. We propose a novel approach which combines the revised feature score criterion with independent component analysis that has been developing recently to further improve the classification performance of gene expression data based on support vector machines. Two sets of gene expression data (colon tumor dataset and leukemia dataset) are examined to confirm that the proposed approach can extract a small quantity of independent components which drastically reduce the dimensionality of the original gene expression data when retaining higher recognition rate. For example, 100% cross-validation accuracy has been achieved with only extracting 2 or 3 independent components from leukemia dataset in our experiments
Starting Page	362
Ending Page	367
File Size	7168573
Page Count	6
File Format	PDF
ISBN	1424406048
DOI	10.1109/ICCIAS.2006.294155
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-11-03
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Computer science Support vector machine classification Independent component analysis Machine learning Gene expression Data mining Neoplasms Principal component analysis Diseases
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

The Classification of Tumor Using Gene Expression Profile Based on Support Vector Machines and Factor Analysis

MRS classification based on independent component analysis and support vector machines

Ensemble Classifiers based on Kernel ICA for Cancer Data Classification

Comparison of Different Feature Extraction Methods on Classification of Gene Expression Data

A Greedy Correlation-Incorporated SVM-Based Algorithm for Gene Selection

Tumor Classification Based on Partial Least Square Regression

A Novel Two-Stage Cancer Classification Method for Microarray Data Based on Supervised Manifold Learning

New Method Based on Support Vector Machine in Classification for Hyperspectral Data

Feature Selection for Cancer Classification Based on Support Vector Machine

Molecular Diagnosis of Tumor Based on Independent Component Analysis and Support Vector Machines

Similar Documents

The Classification of Tumor Using Gene Expression Profile Based on Support Vector Machines and Factor Analysis

MRS classification based on independent component analysis and support vector machines

Ensemble Classifiers based on Kernel ICA for Cancer Data Classification

Comparison of Different Feature Extraction Methods on Classification of Gene Expression Data

A Greedy Correlation-Incorporated SVM-Based Algorithm for Gene Selection

Tumor Classification Based on Partial Least Square Regression

A Novel Two-Stage Cancer Classification Method for Microarray Data Based on Supervised Manifold Learning

New Method Based on Support Vector Machine in Classification for Hyperspectral Data

Feature Selection for Cancer Classification Based on Support Vector Machine

Molecular Diagnosis of Tumor Based on Independent Component Analysis and Support Vector Machines