Utilizing Negative Markers for Identifying Mycobacteria Species based on Mass Spectrometry with Machine Learning Methods Matrixassisted laser desorptionionization timeofflight (MALDITOF) mass spectrometry (MS) is a useful tool for rapid identification of microorganisms based on the protein mass profile represented in a mass spectrum of the microorganism. Typically, markers that are specific for particular microorganisms are extracted from the mass information obtained by MALDITOF MS, and a machine learning technique is applied to the markers. Identification of mycobacteria is of high clinical importance in that different pathogens must be treated with different antibiotics, but is still challenging because spectral patterns of different mycobacteria appear similar. In this paper, we propose a novel approach to use both positive and negative markers in order to enhance discrimination between the spectral patterns of different mycobacteria. We apply the proposed method to classify species in the Mycobacterium abscessus and Mycobacterium fortuitum groups. Experimental results demonstrate that, when combined with various classifier techniques, our method significantly improves the accuracy of mycobacteria identification.

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.