Name
Title | ||
|---|---|---|
Molecular Diagnosis of Tumor Based on Independent Component Analysis and Support Vector Machines |
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 |
Year | DOI | Venue |
|---|---|---|
2006 | null | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
leukemia dataset,colon tumor dataset,pattern classification,original gene expression data,tissue samples,discriminant components,independent component analysis,independent component,molecular biophysics,accurate classification,gene ranking,colon dataset,cross-validation accuracy,gene expression data,molecular tumor diagnosis,efficient tumor diagnosis,tumours,classification performance,medical diagnostic computing,molecular diagnosis,support vector machines,support vector machine,cross validation | Data mining,Pattern recognition,Computer science,Discriminant,Support vector machine,Curse of dimensionality,Independent component analysis,Artificial intelligence,Molecular biophysics,Sample size determination | Conference |
Volume | Issue | ISSN |
4456 LNAI | null | 16113349 |
ISBN | Citations | PageRank |
1-4244-0605-6 | 0 | 0.34 |
References | Authors | |
10 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shu-Lin Wang | 1 | 51 | 6.15 |
| Huo-wang Chen | 2 | 235 | 33.47 |
| Ji Wang | 3 | 140 | 12.56 |
| Dingxing Zhang | 4 | 35 | 4.12 |
| Shutao Li | 5 | 2594 | 139.10 |