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Critical Assessment of Small Molecule Identification 2016: automated methods
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...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5368104/ https://www.ncbi.nlm.nih.gov/pubmed/29086042 http://dx.doi.org/10.1186/s13321-017-0207-1 |
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author | Schymanski, Emma L. Ruttkies, Christoph Krauss, Martin Brouard, Céline Kind, Tobias Dührkop, Kai Allen, Felicity Vaniya, Arpana Verdegem, Dries Böcker, Sebastian Rousu, Juho Shen, Huibin Tsugawa, Hiroshi Sajed, Tanvir Fiehn, Oliver Ghesquière, Bart Neumann, Steffen |
author_facet | Schymanski, Emma L. Ruttkies, Christoph Krauss, Martin Brouard, Céline Kind, Tobias Dührkop, Kai Allen, Felicity Vaniya, Arpana Verdegem, Dries Böcker, Sebastian Rousu, Juho Shen, Huibin Tsugawa, Hiroshi Sajed, Tanvir Fiehn, Oliver Ghesquière, Bart Neumann, Steffen |
author_sort | Schymanski, Emma L. |
collection | PubMed |
description | 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. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-017-0207-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5368104 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-53681042017-04-12 Critical Assessment of Small Molecule Identification 2016: automated methods Schymanski, Emma L. Ruttkies, Christoph Krauss, Martin Brouard, Céline Kind, Tobias Dührkop, Kai Allen, Felicity Vaniya, Arpana Verdegem, Dries Böcker, Sebastian Rousu, Juho Shen, Huibin Tsugawa, Hiroshi Sajed, Tanvir Fiehn, Oliver Ghesquière, Bart Neumann, Steffen J Cheminform Research Article 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. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13321-017-0207-1) contains supplementary material, which is available to authorized users. Springer International Publishing 2017-03-27 /pmc/articles/PMC5368104/ /pubmed/29086042 http://dx.doi.org/10.1186/s13321-017-0207-1 Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Schymanski, Emma L. Ruttkies, Christoph Krauss, Martin Brouard, Céline Kind, Tobias Dührkop, Kai Allen, Felicity Vaniya, Arpana Verdegem, Dries Böcker, Sebastian Rousu, Juho Shen, Huibin Tsugawa, Hiroshi Sajed, Tanvir Fiehn, Oliver Ghesquière, Bart Neumann, Steffen Critical Assessment of Small Molecule Identification 2016: automated methods |
title | Critical Assessment of Small Molecule Identification 2016: automated methods |
title_full | Critical Assessment of Small Molecule Identification 2016: automated methods |
title_fullStr | Critical Assessment of Small Molecule Identification 2016: automated methods |
title_full_unstemmed | Critical Assessment of Small Molecule Identification 2016: automated methods |
title_short | Critical Assessment of Small Molecule Identification 2016: automated methods |
title_sort | critical assessment of small molecule identification 2016: automated methods |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5368104/ https://www.ncbi.nlm.nih.gov/pubmed/29086042 http://dx.doi.org/10.1186/s13321-017-0207-1 |
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