In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids | Zendy

Lars J. Kangas | Zendy; Thomas Metz | Zendy; Giorgis Isaac | Zendy; Brian T. Schrom | Zendy; Bojana Ginovska | Zendy; LuNing Wang | Zendy; Li Tan | Zendy; R. R. Lewis | Zendy; John H. Miller | Zendy

Open Access

In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids

Author(s) -

Lars J. Kangas,

Thomas Metz,

Giorgis Isaac,

Brian T. Schrom,

Bojana Ginovska,

LuNing Wang,

Li Tan,

R. R. Lewis,

John H. Miller

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/bts194

Subject(s) - in silico , fragmentation (computing) , tandem mass spectrometry , mass spectrometry , software , computer science , identification (biology) , artificial intelligence , machine learning , chemistry , computational biology , data mining , chromatography , biology , biochemistry , botany , gene , programming language , operating system

Liquid chromatography-mass spectrometry-based metabolomics has gained importance in the life sciences, yet it is not supported by software tools for high throughput identification of metabolites based on their fragmentation spectra. An algorithm (ISIS: in silico identification software) and its implementation are presented and show great promise in generating in silico spectra of lipids for the purpose of structural identification. Instead of using chemical reaction rate equations or rules-based fragmentation libraries, the algorithm uses machine learning to find accurate bond cleavage rates in a mass spectrometer employing collision-induced dissociation tandem mass spectrometry.

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