Name
Title | ||
|---|---|---|
An efficient mixed-model for screening differentially expressed genes of breast cancer based on LR-RF. |
Abstract | ||
|---|---|---|
To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE45255, were downloaded from GEO. By combining the Logistic Regression and Random Forest algorithm, this paper proposed a novel method named LR-RF to select differentially expressed genes of breast cancer on microarray data by the Bonferroni test of FWER error measure. Comparing with Logistic Regression and Random Forest, our study shows that LR-FR has a great facility in selecting differentially expressed genes. The average prediction accuracy of the proposed LR-RF from replicating random test ten times surprisingly reaches 93.11% with variance as low as 0.00045. The prediction accuracy rate reaches a maximum 95.57% when threshold value in the random forest algorithm process of ranking genes importance score, and the differentially expressed genes are relatively few in number. In addition, through analyzing the gene interaction networks, most of the top 20 genes we selected were found to involve in the development of breast cancer. All of these results demonstrate the reliability and efficiency of LR-RF. It is anticipated that LR-RF would provide new knowledge and method for biologists, medical scientists, and cognitive computing researchers to identify disease-related genes of breast cancer. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCBB.2018.2829519 | IEEE/ACM transactions on computational biology and bioinformatics |
Keywords | Field | DocType |
Breast cancer,Radio frequency,Logistics,Measurement uncertainty,Gene expression,Vegetation | Breast cancer,Bonferroni correction,Computer science,Microarray analysis techniques,Mixed model,Artificial intelligence,Computational biology,Gene regulatory network,Random forest,Logistic regression,Gene expression profiling,Machine learning | Journal |
Volume | Issue | ISSN |
16 | 1 | 1557-9964 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mengmeng Sun | 1 | 12 | 2.30 |
| Tao Ding | 2 | 15 | 8.48 |
| Xu-Qing Tang | 3 | 0 | 1.35 |
| Keming Yu | 4 | 40 | 10.60 |