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Deep learning-based text mining for interpretation of omics data

Project description

Novel text-mining technology for interpretation of omics data

The omics technologies produce Big Data at an increasingly high rate, and their interpretation involves an association between individual entities in the context of molecular networks. These associations are derived, not only from the omics data but importantly, the pre-generated networks created by text mining of millions of scientific articles. The EU-funded DeepTextNet project aims to extract novel information from biomedical literature sources on the type and direction of molecular associations. Specifically, the objective is to build a next-generation text mining technology for relation extraction of molecular interactions that utilises deep learning and uses Big Data for training, as opposed to small manually curated datasets used in current methodologies.


Net EU contribution
€ 207 312,00
Norregade 10
1165 Kobenhavn

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Danmark Hovedstaden Byen København
Activity type
Higher or Secondary Education Establishments
Other funding
€ 207 312,00