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Open AccessArticle

Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models

1
Unité de Mathématiques et Informatique Appliquées de Toulouse, UR 875, INRA, 31326 Castanet Tolosan, France
2
LTCI, Télécom Paris, Institut Polytechnique de Paris, 75634 Paris, France
3
Helsinki Institute for Information Technology HIIT, Department of Computer Science, Aalto University, 00076 Espoo, Finland
*
Author to whom correspondence should be addressed.
Part of this work was commenced during J.R.’s research visit to Telecom ParisTech.
Metabolites 2019, 9(8), 160; https://doi.org/10.3390/metabo9080160
Received: 29 June 2019 / Revised: 30 July 2019 / Accepted: 31 July 2019 / Published: 1 August 2019
(This article belongs to the Special Issue Compound Identification of Small Molecules)
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. View Full-Text
Keywords: metabolite identification; machine learning; structured prediction; kernel methods metabolite identification; machine learning; structured prediction; kernel methods
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Brouard, C.; Bassé, A.; d’Alché-Buc, F.; Rousu, J. Improved Small Molecule Identification through Learning Combinations of Kernel Regression Models. Metabolites 2019, 9, 160.

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