Gutenberg Open Science: A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-9174

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DC Field	Value	Language
dc.contributor.author	Hauptmann, Tony	-
dc.contributor.author	Kramer, Stefan	-
dc.date.accessioned	2023-06-14T08:38:19Z	-
dc.date.available	2023-06-14T08:38:19Z	-
dc.date.issued	2023	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/9191	-
dc.description.abstract	Background Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts, to transform multi-omics data into latent representations. One important parameter is the depth of integration: the point at which the latent representations are computed or merged, which can be either early, intermediate, or late. The literature on integration methods is growing steadily, however, close to nothing is known about the relative performance of these methods under fair experimental conditions and under consideration of different use cases. Results We developed a comparison framework that trains and optimizes multi-omics integration methods under equal conditions. We incorporated early integration, PCA and four recently published deep learning methods: MOLI, Super.FELT, OmiEmbed, and MOMA. Further, we devised a novel method, Omics Stacking, that combines the advantages of intermediate and late integration. Experiments were conducted on a public drug response data set with multiple omics data (somatic point mutations, somatic copy number profiles and gene expression profiles) that was obtained from cell lines, patient-derived xenografts, and patient samples. Our experiments confirmed that early integration has the lowest predictive performance. Overall, architectures that integrate triplet loss achieved the best results. Statistical differences can, overall, rarely be observed, however, in terms of the average ranks of methods, Super.FELT is consistently performing best in a cross-validation setting and Omics Stacking best in an external test set setting. Conclusions We recommend researchers to follow fair comparison protocols, as suggested in the paper. When faced with a new data set, Super.FELT is a good option in the cross-validation setting as well as Omics Stacking in the external test set setting. Statistical significances are hardly observable, despite trends in the algorithms’ rankings. Future work on refined methods for transfer learning tailored for this domain may improve the situation for external test sets. The source code of all experiments is available under https://github.com/kramerlab/Multi-Omics_analysis	en_GB
dc.description.sponsorship	Deutsche Forschungsgemeinschaft (DFG)\|491381577\|Open-Access-Publikationskosten 2022–2024 Universität Mainz - Universitätsmedizin	-
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	004 Informatik	de_DE
dc.subject.ddc	004 Data processing	en_GB
dc.title	A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-9174	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 08 Physik, Mathematik u. Informatik	de
jgu.organisation.number	7940	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	BMC Bioinformatics	de
jgu.journal.volume	24	de
jgu.pages.alternative	45	de
jgu.publisher.year	2023	-
jgu.publisher.name	Springer	de
jgu.publisher.place	London	de
jgu.publisher.issn	1471-2105	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	004	de
jgu.publisher.doi	10.1186/s12859-023-05166-7	de
jgu.organisation.ror	https://ror.org/023b0x485	-
jgu.subject.dfg	Ingenieurwissenschaften	de
Appears in collections:	DFG-491381577-G

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