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DeepTextNet

Financiado

Cerrado

Deep learning based text mining for interpretation of omics data

"The academic community and the pharmaceutical industry use omics technologies to produce big data at an incredibly increasing rate but are faced with major challenges when it comes to their interpretation. Key for this interpreta... ver más

31/10/2023

UCPH

207K€

Presupuesto del proyecto: 207K€

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2023-10-31

MSCA-IF-2020: Individual Fellowships

I+D

Cerrada hace 4 años

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Características del participante

Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

1 Participantes

UCPH

207.31K€ | Lider

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Duración del proyecto: 31 meses Fecha Inicio: 2021-03-19
Fecha Fin: 2023-10-31

Líder del proyecto

KOBENHAVNS UNIVERSITET No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 207K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto "The academic community and the pharmaceutical industry use omics technologies to produce big data at an incredibly increasing rate but are faced with major challenges when it comes to their interpretation. Key for this interpretation is the association between individual entities, which in a biological context means creating molecular networks. These associations cannot be derived from the omics data alone, but rely heavily on pre-generated networks created by text mining of millions of scientific articles. One of the most popular sources of such networks is the STRING database, which currently serves ~100,000 users monthly. Many of these users work with omics data and a major obstacle, which limits potential benefits for them, is that literature-derived networks are made up of ""functional associations"", stating only that two molecules do something together, but neither the interaction type nor the direction. Hence, our hypothesis is that state-of-the-art computational approaches will be able to exploit new possibilities in network biology that emerge from big data. The key objective of DeepTextNet is to extract novel information from the biomedical literature on the type and direction of gene/protein associations. Specifically, a new paradigm will be realized by building a next generation text mining technology for relation extraction of molecular interactions that explicitly utilizes deep learning and, in contrast to current methodology, makes use of big data for training as opposed to small manually curated datasets. This new strategy for obtaining comprehensive molecular networks with both type and direction for the interactions is precisely what is currently missing for the interpretation of omics data. We expect the impact to be high and wide, as on top of applying this strategy on omics datasets as part of the project, the new technology will feed directly into STRING, which is used globally and integrated into workflows in both academia and industry."

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