Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models

Celine Brouard*, Antoine Basse, Florence d'Alche-Buc, Juho Rousu

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Abstract

In small molecule identification from tandem mass (MS/MS) spectra, input-output kernel regression (IOKR) currently provides the state-of-the-art combination of fast training and prediction and high identification rates. The IOKR approach can be simply understood as predicting a fingerprint vector from the MS/MS spectrum of the unknown molecule, and solving a pre-image problem to find the molecule with the most similar fingerprint. In this paper, we bring forward the following improvements to the IOKR framework: firstly, we formulate the IOKRreverse model that can be understood as mapping molecular structures into the MS/MS feature space and solving a pre-image problem to find the molecule whose predicted spectrum is the closest to the input MS/MS spectrum. Secondly, we introduce an approach to combine several IOKR and IOKRreverse models computed from different input and output kernels, called IOKRfusion. The method is based on minimizing structured Hinge loss of the combined model using a mini-batch stochastic subgradient optimization. Our experiments show a consistent improvement of top-k accuracy both in positive and negative ionization mode data.

Original languageEnglish
Article number160
Number of pages14
JournalMETABOLITES
Volume9
Issue number8
DOIs
Publication statusPublished - Aug 2019
MoE publication typeA1 Journal article-refereed

Keywords

  • metabolite identification
  • machine learning
  • structured prediction
  • kernel methods
  • METABOLITE IDENTIFICATION
  • PREDICTION

Equipment

Science-IT

Mikko Hakala (Manager)

School of Science

Facility/equipment: Facility

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