Name
Abstract | ||
|---|---|---|
The improvement in (semi-)automated fragmentation methods for small molecule identification has been substantial. The achieved high rates of correct candidates in the Top 1 and Top 10, despite large candidate numbers, open up great possibilities for high-throughput annotation of untargeted analysis for "known unknowns". As more high quality training data becomes available, the improvements in machine learning methods will likely continue, but the alternative approaches still provide valuable complementary information. Improved integration of experimental context will also improve identification success further for "real life" annotations. The true "unknown unknowns" remain to be evaluated in future CASMI contests. Graphical abstract . |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1186/s13321-017-0207-1 | J. Cheminformatics |
Keywords | Field | DocType |
Compound identification,High resolution mass spectrometry,In silico fragmentation,Metabolomics,Structure elucidation | Training set,Metadata,Annotation,Ranking,Computer science,Input/output,Bioinformatics,Kernel regression | Journal |
Volume | Issue | ISSN |
9 | 1 | 1758-2946 |
Citations | PageRank | References |
6 | 0.51 | 11 |
Authors | ||
17 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emma Schymanski | 1 | 20 | 1.26 |
| Christoph Ruttkies | 2 | 20 | 2.28 |
| Martin Krauss | 3 | 6 | 0.51 |
| Céline Brouard | 4 | 6 | 0.51 |
| Tobias Kind | 5 | 85 | 6.56 |
| Kai Dührkop | 6 | 34 | 4.35 |
| Felicity Allen | 7 | 151 | 9.78 |
| Arpana Vaniya | 8 | 7 | 0.88 |
| Dries Verdegem | 9 | 6 | 0.51 |
| Sebastian Böcker | 10 | 332 | 39.19 |
| Juho Rousu | 11 | 565 | 43.40 |
| Huibin Shen | 12 | 45 | 4.14 |
| Hiroshi Tsugawa | 13 | 21 | 2.07 |
| Tanvir Sajed | 14 | 116 | 5.96 |
| Oliver Fiehn | 15 | 191 | 25.25 |
| Bart Ghesquière | 16 | 6 | 0.51 |
| Steffen Neumann | 17 | 95 | 10.04 |