Name
Abstract | ||
|---|---|---|
Serial Analysis of Gene Expression (SAGE) is a relatively new method for monitoring gene expression levels and is expected to contribute significantly to the progress in cancer treatment by enabling a precise and early diagnosis. A promising application of SAGE gene expression data is classification of tumors. In this paper, we build three event models (the multivariate Bernoulli model, the multinomial model and the normalized multinomial model) for SAGE data classification. Both binary classification and multicategory classification are investigated. Experiments on two SAGE datasets show that the multivariate Bernoulli model performs well with small feature sizes, but the multinomial performs better at large feature sizes, while the normalized multinomial performs well with medium feature sizes. The multinomial achieves the highest overall accuracy. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11758525_104 | International Conference on Computational Science (2) |
Keywords | Field | DocType |
tumor classification,multicategory classification,sage data classification,normalized multinomial model,event model,binary classification,multinomial model,sage datasets,multivariate bernoulli model,sage gene expression data,normalized multinomial,serial analysis of gene expression,gene expression | Data mining,Data modeling,Normalization (statistics),Binary classification,Computer science,Multivariate statistics,Multinomial distribution,Multicategory,Data classification,Serial analysis of gene expression | Conference |
Volume | ISSN | ISBN |
3992 | 0302-9743 | 3-540-34381-4 |
Citations | PageRank | References |
1 | 0.36 | 8 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xin Jin | 1 | 333 | 62.83 |
| Anbang Xu | 2 | 351 | 30.52 |
| Guoxing Zhao | 3 | 16 | 3.43 |
| Ma Jixin | 4 | 206 | 32.69 |
| Rongfang Bie | 5 | 547 | 68.23 |