Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID
| العنوان: | Metabolite Identification through Machine Learning— Tackling CASMI Challenge Using FingerID |
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| المؤلفون: | Juho Rousu, Huibin Shen, Nicola Zamboni, Markus Heinonen |
| المساهمون: | Aalto-yliopisto, Aalto University |
| المصدر: | Metabolites, Vol 3, Iss 2, Pp 484-505 (2013) Metabolites Metabolites; Volume 3; Issue 2; Pages: 484-505 |
| بيانات النشر: | MDPI AG, 2013. |
| سنة النشر: | 2013 |
| مصطلحات موضوعية: | Identification methods, Web server, Computer science, Endocrinology, Diabetes and Metabolism, lcsh:QR1-502, computer.software_genre, Machine learning, 01 natural sciences, Biochemistry, Bottleneck, Article, lcsh:Microbiology, Set (abstract data type), 03 medical and health sciences, metabolite identification, molecular fingerprints, machine learning, FingerID, Molecular Biology, 030304 developmental biology, 0303 health sciences, business.industry, 010401 analytical chemistry, Rank (computer programming), 0104 chemical sciences, Identification (information), Filter (video), Data mining, Artificial intelligence, business, computer, PubChem |
| الوصف: | 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. |
| وصف الملف: | application/pdf |
| اللغة: | English |
| تدمد: | 2218-1989 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::00a6a271ccaa161319749fa404bdac43 http://www.mdpi.com/2218-1989/3/2/484 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....00a6a271ccaa161319749fa404bdac43 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 22181989 |
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