Identifying the minimum amplicon sequence depth to adequately predict classes in eDNA-based marine biomonitoring using supervised machine learning
| Author: | Verena Dully, Thomas A. Wilding, Timo MühlhausORCiD, Thorsten StoeckORCiD |
|---|---|
| URL: | https://www.sciencedirect.com/science/article/pii/S2001037021001148?via%3Dihub |
| DOI: | https://doi.org/10.1016/j.csbj.2021.04.005 |
| ISSN: | 2001-0370 |
| Journal: | Computational and Structural Biotechnology Journal |
| Publisher: | Elsevier |
| Document Type: | Research Article |
| Language: | English |
| Year of first Publication: | 2021 |
| Release Date: | 2022/05/13 |
| Volume: | 19 |
| Page Number: | 13 |
| First Page: | 2256 |
| Last Page: | 2268 |
| Faculties / Organisational entities: | RPTU in Kaiserslautern / Fachbereich Biologie / Ökologie |
| Open access state: | Gold Open-Access |
| RPTU: | Kaiserslautern |
| Research funding: | DFG |
| Sonstige | |
| Created at the RPTU: | Yes |