Name
Abstract | ||
|---|---|---|
Molecular measurements from cancer patients such as gene expression and DNA methylation can be influenced by several external factors. This makes it harder to reproduce the exact values of measurements coming from different laboratories. Furthermore, some cancer types are very heterogeneous, meaning that there might be different underlying causes for the same type of cancer among different individuals. If a model does not take potential biases in the data into account, this can lead to problems when trying to predict the stage of a certain cancer type. This is especially true when these biases differ between the training and test set. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-662-44753-6_26 | WABI |
Keywords | Field | DocType |
Cancer biomarkers,Ensemble methods,Gaussian processes,Machine learning,Supervised prediction,Support vector machines | Open data,Deep sequencing,Computer science,Support vector machine,Test data,Open science,Bioinformatics,Cancer biomarkers,Sanger sequencing,Ensemble learning | Conference |
Volume | Issue | ISSN |
17 | 1 | 1471-2164 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Adrin Jalali | 1 | 12 | 3.04 |
| Nico Pfeifer | 2 | 259 | 26.24 |