Multiomics Data Integration for Gene Regulatory Network Inference with Exponential Family Embeddings
| Autor: | Surabhi Jagtap, Abdulkadir Celikkanat, Aurelic Piravre, Frederiuue Bidard, Laurent Duval, Fragkiskos D. Malliaros |
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| Přispěvatelé: | IFP Energies nouvelles (IFPEN), Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay, OPtimisation Imagerie et Santé (OPIS), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de vision numérique (CVN), Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Université Paris-Saclay-CentraleSupélec-Université Paris-Saclay |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0303 health sciences
03 medical and health sciences representation learning omics data integration [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] network embedding 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing random walks 02 engineering and technology [INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM] 030304 developmental biology multilayer network |
| Zdroj: | EUSIPCO-29th European Signal Processing Conference EUSIPCO-29th European Signal Processing Conference, Aug 2021, Dublin / Online, Ireland |
| Popis: | International audience; The advent of omics technologies has enabled the generation of huge, complex, heterogeneous, and high-dimensional omics data. Imposing numerous challenges in data integration, these data could lead to a better understanding of the organism's cellular system. Omics data are typically represented as networks to study relations between biological entities, such as protein-protein interaction, gene regulation, and signal transduction. To this end, network embedding approaches allow us to learn latent feature representations for nodes of a graph structure. In this study, we propose a new methodology to learn embeddings by modeling the underlying interactions among biological entities (nodes) with exponential family distributions from a well-chosen set of omics modalities. We evaluate our proposed method based on the gene regulatory network (GRN) inference problem. As the ground truth for evaluation, we use GRN available in public databases and demonstrate its effectiveness by comparing to other network integration approaches. |
| Databáze: | OpenAIRE |
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