Name
Abstract | ||
|---|---|---|
Due to the large number of uncertain factors in hybridization, image capture and processing of the microarray, multiple probes were generally arranged to improve the reliability of the measurement. However, the small area limited the number of probes that were allowed to be added on, so a composite probe would be the better choice. A composite probe contained the linear combination of a variety of gene fragments. It was used so that the microarray could easily realize the repeated gene fragments within a limited region. The number of composite probes would rapidly dwindle when it compared to a traditional microarray. At the same time, since the sparse characteristics of biological gene mutation, the compressed sensing idea is adopted to recovery the gene variation in the composite probes. The 96 fragments can be used with the 48 x 96 sparse random matrix to construct the 48 composite probes when the sparsest level K is no more than 12. Simulation results show that compressed sensing can accurately recover the gene mutation by using the Orthogonal Matching Pursuit (OMP) algorithm. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-48490-7_2 | GENETIC AND EVOLUTIONARY COMPUTING |
Keywords | Field | DocType |
Compressed sensing,Microarray,Composite probe,Sparse random matrix,OMP | Matching pursuit,Linear combination,Microarray,Pattern recognition,Computer science,Signal recovery,Composite number,Image capture,Artificial intelligence,Compressed sensing,Machine learning,Random matrix | Conference |
Volume | ISSN | Citations |
536 | 2194-5357 | 0 |
PageRank | References | Authors |
0.34 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenhua Gan | 1 | 0 | 1.01 |
| Baoping Xiong | 2 | 0 | 2.70 |
| Fumin Zou | 3 | 3 | 7.16 |
| Yueming Gao | 4 | 0 | 0.68 |
| Min Du | 5 | 0 | 0.68 |