SIMPLE Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra

Dai Hai Nguyen*, Canh Hao Nguyen, Hiroshi Mamitsuka

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
74 Downloads (Pure)

Abstract

Motivation: Recent success in metabolite identification from tandem mass spectra has been led by machine learning, which has two stages: mapping mass spectra to molecular fingerprint vectors and then retrieving candidate molecules from the database. In the first stage, i.e. fingerprint prediction, spectrum peaks are features and considering their interactions would be reasonable for more accurate identification of unknown metabolites. Existing approaches of fingerprint prediction are based on only individual peaks in the spectra, without explicitly considering the peak interactions. Also the current cutting-edge method is based on kernels, which are computationally heavy and difficult to interpret. Results: We propose two learning models that allow to incorporate peak interactions for fingerprint prediction. First, we extend the state-of-the-art kernel learning method by developing kernels for peak interactions to combine with kernels for peaks through multiple kernel learning (MKL). Second, we formulate a sparse interaction model for metabolite peaks, which we call SIMPLE, which is computationally light and interpretable for fingerprint prediction. The formulation of SIMPLE is convex and guarantees global optimization, for which we develop an alternating direction method of multipliers (ADMM) algorithm. Experiments using the MassBank dataset show that both models achieved comparative prediction accuracy with the current top-performance kernel method. Furthermore SIMPLE clearly revealed individual peaks and peak interactions which contribute to enhancing the performance of fingerprint prediction.

Original languageEnglish
Pages (from-to)i323-i332
Number of pages10
JournalBioinformatics
Volume34
Issue number13
DOIs
Publication statusPublished - 1 Jul 2018
MoE publication typeA1 Journal article-refereed
EventAnnual Conference on Intelligent Systems for Molecular Biology - Chicago, United States
Duration: 6 Jul 201810 Jul 2018
Conference number: 26

Keywords

  • ALGORITHMS
  • KERNELS

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