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Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID
Helsinki Institute for Information Technology HIIT; Department of Information and Computer Science, Aalto University, Konemiehentie 2, FI-02150 Espoo, Finland;
Institute of Molecular Systems Biology, ETH Zürich, Wolfgang-Pauli Street 16, 8093 Zürich, Switzerland
IBISC, Université d'Evry-Val d'Essonne, Bâtiment IBGBI, 23 Bd de France, 91037 cedex Evry,France
* Author to whom correspondence should be addressed.
Received: 1 April 2013; in revised form: 24 May 2013 / Accepted: 30 May 2013 / Published: 6 June 2013
Abstract: Metabolite identification is a major bottleneck in metabolomics due to the number and diversity of the molecules. To alleviate this bottleneck, computational methods and tools that reliably filter the set of candidates are needed for further analysis by human experts. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for developing a new genre of metabolite identification methods that rely on machine learning as the primary vehicle for identification. In this paper we describe the machine learning approach used in FingerID, its application to the CASMI challenges and some results that were not part of our challenge submission. In short, FingerID learns to predict molecular fingerprints from a large collection of MS/MS spectra, and uses the predicted fingerprints to retrieve and rank candidate molecules from a given large molecular database. Furthermore, we introduce a web server for FingerID, which was applied for the first time to the CASMI challenges. The challenge results show that the new machine learning framework produces competitive results on those challenge molecules that were found within the relatively restricted KEGG compound database. Additional experiments on the PubChem database confirm the feasibility of the approach even on a much larger database, although room for improvement still remains.
Keywords: metabolite identification; molecular fingerprints; machine learning; FingerID
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MDPI and ACS Style
Shen, H.; Zamboni, N.; Heinonen, M.; Rousu, J. Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID. Metabolites 2013, 3, 484-505.
Shen H, Zamboni N, Heinonen M, Rousu J. Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID. Metabolites. 2013; 3(2):484-505.
Shen, Huibin; Zamboni, Nicola; Heinonen, Markus; Rousu, Juho. 2013. "Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID." Metabolites 3, no. 2: 484-505.