M. Wang, J. J. Carver, V. V. Phelan, L. M. Sanchez, N. Garg et al., Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking, Nat. Biotechnol, vol.34, pp.828-837, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01371824

H. Horai, M. Arita, S. Kanaya, Y. Nihei, T. Ikeda et al., MassBank: A public repository for sharing mass spectral data for life sciences, J. Mass Spectrom, vol.45, pp.703-714, 2010.

D. H. Nguyen, C. H. Nguyen, and H. Mamitsuka, Recent advances and prospects of computational methods for metabolite identification: A review with emphasis on machine learning approaches, Briefings Bioinform, 2018.

M. Heinonen, H. Shen, N. Zamboni, and J. Rousu, Metabolite identification and molecular fingerprint prediction through machine learning, Bioinformatics, vol.28, pp.2333-2341, 2012.

H. Shen, N. Zamboni, M. Heinonen, and J. Rousu, Metabolite identification through machine learning-Tackling CASMI challenge using fingerID, vol.3, pp.484-505, 2013.

Y. Djoumbou-feunang, A. Pon, N. Karu, J. Zheng, C. Li et al., Significantly Improved ESI-MS/MS Prediction and Compound Identification. Metabolites, vol.9, 2019.

F. Allen, A. Pon, M. Wilson, R. Greiner, D. Wishart et al., A web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra, Nucleic Acids Res, vol.42, pp.94-99, 2014.

K. Dührkop, H. Shen, M. Meusel, J. Rousu, and S. Böcker, Searching molecular structure databases with tandem mass spectra using CSI:FingerID, Proc. Natl. Acad. Sci, vol.112, pp.12580-12585, 2015.

C. Brouard, H. Shen, K. Dührkop, F. ;-d'alché-buc, S. Böcker et al., Fast metabolite identification with Input Output Kernel Regression, Bioinformatics, vol.32, pp.28-36, 2016.

C. Brouard, E. Bach, S. Böcker, and J. Rousu, Magnitude-preserving ranking for structured outputs, Proceedings of the Asian Conference on Machine Learning, pp.15-17, 2017.

I. Laponogov, N. Sadawi, D. Galea, R. Mirnezami, and K. A. Veselkov, ChemDistiller: an engine for metabolite annotation in mass spectrometry, Bioinformatics, vol.34, pp.2096-2102, 2018.

D. H. Nguyen, C. H. Nguyen, and H. Mamitsuka, SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra, Bioinformatics, vol.34, pp.323-332, 2018.

D. H. Nguyen, C. H. Nguyen, and H. Mamitsuka, ADAPTIVE: leArning DAta-dePendenT, concIse molecular VEctors for fast, accurate metabolite identification from tandem mass spectra, Bioinformatics, vol.35, pp.164-172, 2019.

K. Dührkop, M. Fleischauer, M. Ludwig, A. A. Aksenov, A. V. Melnik et al., SIRIUS 4: A rapid tool for turning tandem mass spectra into metabolite structure information, Nat. Methods, vol.16, pp.299-302, 2019.

, CSI:FingerID Passed 10 Million Compound Queries, p.26, 2019.

E. L. Schymanski, C. Ruttkies, M. Krauss, C. Brouard, T. Kind et al., Critical assessment of small molecule identification 2016: Automated methods, J. Cheminform, vol.9, 2017.

C. Webpage-of, , p.31, 2017.

S. Wolf, S. Schmidt, M. Müller-hannemann, and S. Neumann, In silico fragmentation for computer assisted identification of metabolite mass spectra, BMC Bioinform, vol.11, 2010.

C. Ruttkies, E. L. Schymanski, S. Wolf, J. Hollender, and S. Neumann, MetFrag relaunched: incorporating strategies beyond in silico fragmentation, J. Cheminform, vol.8, issue.3, 2016.

H. Shen, K. Dührkop, S. Böcker, and J. Rousu, Metabolite identification through multiple kernel learning on fragmentation trees, Bioinform, vol.30, pp.157-164, 2014.

G. H. Bakir, T. Hofmann, B. Schölkopf, A. J. Smola, and B. Taskar, Neural Information Processing

M. The and . Press, , 2007.

C. Brouard, M. Szafranski, and F. ;-d'alché-buc, Output Kernel Regression: supervised and semi-supervised structured output prediction with operator-valued kernels, J. Mach. Learn. Res, vol.17, pp.1-48, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01216708

C. Cortes, M. Mohri, and A. Rostamizadeh, Algorithms for Learning Kernels Based on Centered Alignment, J. Mach. Learn. Res, vol.13, pp.795-828, 2012.

T. Hazan, J. Keshet, and D. A. Mcallester, Direct loss minimization for structured prediction, Proceeding of Advances in Neural Information Processing Systems, pp.6-11, 2010.

E. Bolton, Y. Wang, P. Thiessen, and S. Bryant, Chapter 12-PubChem: Integrated platform of small molecules and biological activities, Annu. Rep. Comput. Chem, vol.4, pp.217-241, 2008.

M. Radovanovic, A. Nanopoulos, and M. Ivanovic, Hubs in Space: Popular Nearest Neighbors in High-Dimensional Data, J. Mach. Learn. Res, vol.11, pp.2487-2531, 2010.

Y. Shigeto, I. Suzuki, K. Hara, M. Shimbo, and Y. Matsumoto, Ridge regression, hubness, and zero-shot learning, Machine Learning and Knowledge Discovery in Databases, pp.135-151, 2015.

H. Larochelle, D. Erhan, and Y. Bengio, Zero-data Learning of New Tasks, Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, pp.646-651, 2008.

Y. Xian, B. Schiele, and Z. Akata, Zero-Shot Learning-The Good, the Bad and the Ugly, Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017), pp.3077-3086, 2017.

T. Baltru?aitis, C. Ahuja, and L. P. Morency, Multimodal machine learning: A survey and taxonomy, IEEE Trans. Pattern Anal. Mach. Intell, vol.41, pp.423-443, 2018.

I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large margin methods for structured and interdependent output variables, J. Mach. Learn. Res, vol.6, pp.1453-1484, 2005.

S. Böcker and F. Rasche, Towards de novo identification of metabolites by analyzing tandem mass spectra, Bioinfomatics, vol.24, pp.49-55, 2008.

S. Böcker and K. Dührkop, Fragmentation trees reloaded, J. Cheminform, vol.8, issue.5, 2016.

K. Dührkop, Computational Methods for Small Molecule Identification, 2018.

L. Ralaivola, S. Swamidass, H. Saigo, and P. Baldi, Graph kernels for chemical informatics, Neural Netw, vol.18, pp.1093-1110, 2005.

E. L. Willighagen, J. W. Mayfield, J. Alvarsson, A. Berg, L. Carlsson et al., The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searching, J. Cheminform, vol.9, p.33, 2017.

J. Klekota and F. Roth, Chemical substructures that enrich for biological activity, Bioinformatics, vol.24, pp.2518-2525, 2008.

D. Rogers and M. Hahn, Extended-Connectivity Fingerprints, J. Chem. Inf. Model, vol.50, pp.742-754, 2010.