Name
Title | ||
|---|---|---|
Improve Glioblastoma Multiforme Prognosis Prediction by Using Feature Selection and Multiple Kernel Learning. |
Abstract | ||
|---|---|---|
Glioblastoma multiforme (GBM) is a highly aggressive type of brain cancer with very low median survival. In order to predict the patient's prognosis, researchers have proposed rules to classify different glioma cancer cell subtypes. However, survival time of different subtypes of GBM is often various due to different individual basis. Recent development in gene testing has evolved classic subtype ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TCBB.2016.2551745 | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Keywords | Field | DocType |
Prognostics and health management,Cancer,Predictive models,Mathematical model,Biological system modeling,Data models,Bioinformatics | Data modeling,Feature selection,Prognostics,Computer science,Multiple kernel learning,Minimum redundancy feature selection,Data type,Artificial intelligence,Bioinformatics,Kernel method,Cancer,Machine learning | Journal |
Volume | Issue | ISSN |
13 | 5 | 1545-5963 |
Citations | PageRank | References |
2 | 0.37 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ya Zhang | 1 | 9 | 1.27 |
| Ao Li | 2 | 211 | 25.18 |
| Chen Peng | 3 | 8 | 5.25 |
| Minghui Wang | 4 | 86 | 9.71 |