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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Barbiero, P. et al. (2020). Unsupervised Multi-omic Data Fusion: The Neural Graph Learning Network. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_15
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