Name
Abstract | ||
|---|---|---|
Cancer leads to approximately 25% of all mortalities, making it the second leading cause of death in the United States. Early and accurate detection of cancer is critical to the well being of patients. Analysis of gene expression data leads to cancer identification and classification, which will facilitate proper treatment selection and drug development. Gene expression data sets for ovarian, prostate, and lung cancer were analyzed in this research. An integrated gene-search algorithm for genetic expression data analysis was proposed. This integrated algorithm involves a genetic algorithm and correlation-based heuristics for data preprocessing (on partitioned data sets) and data mining (decision tree and support vector machines algorithms) for making predictions. Knowledge derived by the proposed algorithm has high classification accuracy with the ability to identify the most significant genes. Bagging and stacking algorithms were applied to further enhance the classification accuracy. The results were compared with that reported in the literature. Mapping of genotype information to the phenotype parameters will ultimately reduce the cost and complexity of cancer detection and classification. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1016/j.compbiomed.2006.01.007 | Comp. in Bio. and Med. |
Keywords | DocType | Volume |
cancer identification,lung cancer,genetic expression,prostate cancer,integrated algorithm,partitioned data set,genetic algorithm,cancer gene search,gene expression data,data mining,classification accuracy,ovarian cancer,gene expression data set,cancer detection,genetic expression data analysis,gene selection | Journal | 37 |
Issue | ISSN | Citations |
2 | 0010-4825 | 29 |
PageRank | References | Authors |
1.70 | 11 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shital Shah | 1 | 75 | 6.29 |
| A. Kusiak | 2 | 724 | 111.33 |