In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids.
| Title | In silico identification software (ISIS): a machine learning approach to tandem mass spectral identification of lipids. |
| Publication Type | Journal Article |
| Year of Publication | 2012 |
| Authors | Kangas LJ, Metz TO, Isaac G, Schrom BT, Ginovska-Pangovska B, Wang L, Tan L, Lewis RR, Miller JH |
| Journal | Bioinformatics |
| Keywords | Algorithms, Artificial Intelligence, Computer Simulation, Lipids, Metabolomics, Sensitivity and Specificity, Software, Tandem Mass Spectrometry |
| Abstract | MOTIVATION: 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. RESULTS: A preliminary test of the algorithm with 45 lipids from a subset of lipid classes shows both high sensitivity and specificity. |
| DOI | 10.1093/bioinformatics/bts194 |
| Alternate Journal | Bioinformatics |
| PubMed ID | 22592377 |
| PubMed Central ID | PMC3381961 |
| Grant List | DK071283 / DK / NIDDK NIH HHS / United States R33 DK071283 / DK / NIDDK NIH HHS / United States U54AI081680 / AI / NIAID NIH HHS / United States UL1 TR000128 / TR / NCATS NIH HHS / United States |