Classification based Integration of Quantifications for LCMS Analysis A classification based integration of quantification method for the Liquid Chromatography xe2x80x93 Mass Spectrometry (LCMS) analysis is described in this paper. Typically, one biological tissue may be sent to the LCMS many times in practice to generate multiple LCMS data. Due to the precise level or the profile of the search engine, these multiple individual quantitative results of the multiple LCMS data may be partially identical. Here we proposed a method to integrate the quantitative results for the case where there are multiple individual measurements but the results are only partially identical. This proposed method applies a classifier to the peptides and treats the predicted probabilities of the classification as the weights to combine these multiple individual quantitative results into a better one. Experimental results show that in the task of quantitative LCMS, the results generated by this integration method perform better than the ones produced by other individual measurements.

Metabolite Identification Using Artificial Neural Network Metabolite identification is a major bottleneck in mass spectrometrybased metabolomic studies. In a typical untargeted metabolomic study that uses tandem MS (MSMS), the identities of metabolites are determined by matching the experimental MSMS data against those in spectral libraries. However, existing spectral libraries cover only a small fraction of known compounds. We introduce a neural network model to predict the identity of analytes whose reference measurements are not available in spectral libraries. Specifically, an artificial neural network model is trained to predict the fingerprints of compounds based on their experimental MSMS spectra. Candidates are retrieved from metabolite databases based on molecular formula or precursor mass. The candidate with the most similar fingerprint is chosen as the predicted one. The method was evaluated via an independent dataset. The results show that our neural network model improves MSMSbased metabolite identification accuracy by a considerable margin compared to previous computational methods, such as Metfrag and CSI:FingerID.