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Chapitre D'ouvrage Année : 2020

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

Pietro Barbiero
Marta Lovino
Mattia Siviero
Vincenzo Randazzo
Elisa Ficarra


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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Dates et versions

hal-03845664 , version 1 (09-11-2022)



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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