title: Artificial neural network for charge prediction in metabolite identification by mass spectrometry.
abstract: Collision-induced dissociation (CID) is widely used in mass spectrometry to identify biologically important molecules by gaining information about their internal structure. Interpretation of experimental CID spectra always involves some form of in silico spectra of potential candidate molecules. Knowledge of how charge is distributed among fragments is an important part of CID simulations that generate in silico spectra from the chemical structure of the precursor ions entering the collision chamber. In this chapter we describe a method to obtain this knowledge by machine learning.
authors.affiliation: Washington State University Tri-Cities, Richland, WA, 99354, USA, jhmiller@tricity.wsu.edu.
authors.forename: J H
authors.initials: JH
authors.lastname: Miller
authors.forename: B T
authors.initials: BT
authors.lastname: Schrom
authors.forename: L J
authors.initials: LJ
authors.lastname: Kangas
mesh_terms: D050356:Lipid Metabolism; D013058:Mass Spectrometry / Q000379:methods*; D008958:Models, Molecular; D009010:Monte Carlo Method; D016571:Neural Networks, Computer; D011506:Proteins / Q000737:chemistry / Q000302:isolation & purification*; D012984:Software
chemical_list: D011506:Proteins