Critical Assessment of Small Molecule Identification 2016: automated methods

Kuvaus

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 .
Näytä enemmän

Julkaisuvuosi

2017

Aineiston tyyppi

Tekijät

Department of Computer Science

Arpana Vaniya - Tekijä

Bart Ghesquière - Tekijä

Celine Brouard - Tekijä

Christoph Ruttkies - Tekijä

Dries Verdegem - Tekijä

Emma L. Schymanski - Tekijä

Felicity Allen - Tekijä

Hiroshi Tsugawa - Tekijä

Huibin Shen - Tekijä

Juho Rousu Orcid -palvelun logo - Tekijä

Kai Dührkop - Tekijä

Martin Krauss - Tekijä

Oliver Fiehn - Tekijä

Sebastian Böcker - Tekijä

Steffen Neumann - Tekijä

Tanvir Sajed - Tekijä

Tobias Kind - Tekijä

Friedrich Schiller University Jena - Muu tekijä

Helmholtz Centre for Environmental Research - Muu tekijä

ICRI - Muu tekijä

King Abdulaziz University - Muu tekijä

Leibniz Institute of Plant Biochemistry - Muu tekijä

RIKEN (Japan) - Muu tekijä

Swiss Federal Institute of Aquatic Science and Technology - Muu tekijä

University of Alberta - Muu tekijä

University of California - Muu tekijä

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