Name
Abstract | ||
|---|---|---|
Predicting disease-related genes is helpful for understanding the disease pathology and the molecular mechanisms during the disease progression. However, traditional methods are not suitable for screening genes related to the disease development, because there are some samples with weak label information in the disease dataset and a small number of genes are known disease-related genes. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-019-3078-9 | BIBM |
Keywords | Field | DocType |
Weakly supervised learning model, Differentially expressed genes, Disease-related genes, Transductive support vector machine, The difference kernel function | Convergence (routing),Transduction (machine learning),Disease,Gene,Computer science,Support vector machine,Supervised learning,Disease progression,Artificial intelligence,Machine learning,Kernel (statistics) | Conference |
Volume | Issue | ISSN |
20 | 16 | 1471-2105 |
ISBN | Citations | PageRank |
978-1-5386-5489-7 | 1 | 0.35 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Zhang | 1 | 123 | 28.55 |
| Xueting Huo | 2 | 1 | 0.35 |
| Xia Guo | 3 | 1 | 2.04 |
| Xin Su | 4 | 1 | 0.69 |
| Xiongwen Quan | 5 | 3 | 3.08 |
| Chen Jin | 6 | 3 | 1.39 |