Metabolite identification and molecular fingerprint prediction through machine learning | Zendy

Markus Hein | Zendy; Huibin Shen | Zendy; Nicola Zamboni | Zendy; Juho Rousu | Zendy

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Metabolite identification and molecular fingerprint prediction through machine learning

Author(s) -

Markus Hein,

Huibin Shen,

Nicola Zamboni,

Juho Rousu

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts437

Subject(s) - pubchem , metabolomics , computer science , identification (biology) , support vector machine , python (programming language) , metabolite , artificial intelligence , data mining , machine learning , computational biology , chemistry , bioinformatics , biology , biochemistry , botany , operating system

Metabolite identification from tandem mass spectra is an important problem in metabolomics, underpinning subsequent metabolic modelling and network analysis. Yet, currently this task requires matching the observed spectrum against a database of reference spectra originating from similar equipment and closely matching operating parameters, a condition that is rarely satisfied in public repositories. Furthermore, the computational support for identification of molecules not present in reference databases is lacking. Recent efforts in assembling large public mass spectral databases such as MassBank have opened the door for the development of a new genre of metabolite identification methods.

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