A detailed comparison of analysis processes for MCC-IMS data in disease classification - automated methods can replace manual peak annotations
- The best fully automated analysis process achieves even better classification results than the established manual process. The best algorithms for the three analysis steps are (i) SGLTR (Savitzky-Golay Laplace operator filter thresholding regions) and LM (Local Maxima) for automated peak identification, (ii) EM clustering (Expectation Maximization) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) for the clustering step and (iii) RF (Random Forest) for multivariate classification. Thus, automated methods can replace the manual steps in the analysis process to enable an unbiased high throughput use of the technology.
| Author of HS Reutlingen | Baumbach, Jörg Ingo |
|---|---|
| URN: | urn:nbn:de:bsz:rt2-opus4-14870 |
| DOI: | https://doi.org/10.1371/journal.pone.0184321 |
| eISSN: | 1932-6203 |
| Erschienen in: | PLOS ONE |
| Publisher: | PLOS |
| Place of publication: | Lawrence, Kan. |
| Document Type: | Journal article |
| Language: | English |
| Publication year: | 2017 |
| Volume: | 12 |
| Issue: | 9 |
| Page Number: | 16 |
| First Page: | 1 |
| Last Page: | 16 |
| DDC classes: | 610 Medizin, Gesundheit |
| Open access?: | Ja |
| Licence (German): |  Creative Commons - Namensnennung |
