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Chapitre d'ouvrage

Unsupervised Multi-omic Data Fusion: The Neural Graph Learning Network

Abstract : In recent years, due to the high availability of omic data, data driven biology has greatly expanded. However, the analysis of different data sources is still an open challenge. A few multi-omic approaches have been proposed in literature. However, none of them take into consideration the intrinsic topology of each omic. In this work, an unsupervised learning method based on a deep neural network is proposed. For each omic, a separate network is trained, whose outputs are fused into a single graph; for this purpose, an innovative loss function has been designed to better represent the data cluster manifolds. A graph adjacency matrix is exploited to determine similarities among samples. With this approach, omics having a different number of features are merged into a unique representation. Quantitative and qualitative analyses show that the proposed method has results comparable to the state of the art. The method has a great intrinsic flexibility as it can be customized according to the complexity of the tasks and it has a lot of room for future improvements compared to more fine-tuned methods, opening the way for future research.
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Chapitre d'ouvrage
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https://hal-u-picardie.archives-ouvertes.fr/hal-03845664
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Soumis le : mercredi 9 novembre 2022 - 16:29:07
Dernière modification le : jeudi 10 novembre 2022 - 03:09:41

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Pietro Barbiero, Marta Lovino, Mattia Siviero, Gabriele Ciravegna, Vincenzo Randazzo, et al.. Unsupervised Multi-omic Data Fusion: The Neural Graph Learning Network. Intelligent Computing Theories and Application, 12463, Springer International Publishing, pp.172-182, 2020, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-60799-9_15⟩. ⟨hal-03845664⟩

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