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Metabolite Identification through Machine Learning- Tackling CASMI Challenge Using FingerID

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Shen, Huibin
dc.contributor.author Zamboni, Nicola
dc.contributor.author Heinonen, Markus
dc.contributor.author Rousu, Juho
dc.date.accessioned 2017-05-11T08:40:01Z
dc.date.available 2017-05-11T08:40:01Z
dc.date.issued 2013
dc.identifier.citation Shen , H , Zamboni , N , Heinonen , M & Rousu , J 2013 , ' Metabolite Identification through Machine Learning- Tackling CASMI Challenge Using FingerID ' , METABOLITES , vol. 3 , no. 2 , pp. 484-505 . https://doi.org/10.3390/metabo3020484 en
dc.identifier.other PURE UUID: 9576265b-0d67-493c-bf5e-f4707f8fbdd8
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/9576265b-0d67-493c-bf5e-f4707f8fbdd8
dc.identifier.other PURE LINK: http://www.mdpi.com/2218-1989/3/2/484
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/12713305/metabolites_03_00484.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/25702
dc.description.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. en
dc.format.extent 484-505
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries METABOLITES en
dc.relation.ispartofseries Volume 3, issue 2 en
dc.rights openAccess en
dc.title Metabolite Identification through Machine Learning- Tackling CASMI Challenge Using FingerID en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.subject.keyword metabolite identification
dc.subject.keyword molecular fingerprints
dc.subject.keyword machine learning
dc.subject.keyword FingerID
dc.identifier.urn URN:NBN:fi:aalto-201705114086
dc.identifier.doi 10.3390/metabo3020484
dc.type.version publishedVersion

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