Critical Assessment of Small Molecule Identification 2016: automated methods
Posted on 2017-03-27 - 05:00
Abstract Background The fourth round of the Critical Assessment of Small Molecule Identification (CASMI) Contest ( www.casmi-contest.org ) was held in 2016, with two new categories for automated methods. This article covers the 208 challenges in Categories 2 and 3, without and with metadata, from organization, participation, results and post-contest evaluation of CASMI 2016 through to perspectives for future contests and small molecule annotation/identification. Results The Input Output Kernel Regression (CSI:IOKR) machine learning approach performed best in “Category 2: Best Automatic Structural Identification—In Silico Fragmentation Only”, won by Team Brouard with 41% challenge wins. The winner of “Category 3: Best Automatic Structural Identification—Full Information” was Team Kind (MS-FINDER), with 76% challenge wins. The best methods were able to achieve over 30% Top 1 ranks in Category 2, with all methods ranking the correct candidate in the Top 10 in around 50% of challenges. This success rate rose to 70% Top 1 ranks in Category 3, with candidates in the Top 10 in over 80% of the challenges. The machine learning and chemistry-based approaches are shown to perform in complementary ways. Conclusions 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 .
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Schymanski, Emma; Ruttkies, Christoph; Krauss, Martin; Brouard, Céline; Kind, Tobias; Dührkop, Kai; et al. (2017). Critical Assessment of Small Molecule Identification 2016: automated methods. figshare. Collection. https://doi.org/10.6084/m9.figshare.c.3727522.v1
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AUTHORS (17)
ES
Emma Schymanski
CR
Christoph Ruttkies
MK
Martin Krauss
CB
Céline Brouard
TK
Tobias Kind
KD
Kai Dührkop
FA
Felicity Allen
AV
Arpana Vaniya
DV
Dries Verdegem
SB
Sebastian Böcker
JR
Juho Rousu
HS
Huibin Shen
HT
Hiroshi Tsugawa
TS
Tanvir Sajed
OF
Oliver Fiehn
BG
Bart Ghesquière
SN
Steffen Neumann