{"id":125108,"date":"2021-07-16T11:00:21","date_gmt":"2021-07-16T10:00:21","guid":{"rendered":"https:\/\/eurecat.org\/plataforma-digital-processar-dades-omiques-gran-escala\/"},"modified":"2022-07-25T12:28:07","modified_gmt":"2022-07-25T11:28:07","slug":"digital-platform-to-be-developed-to-process-large-scale-omics-datasets-using-big-data-and-artificial-intelligence","status":"publish","type":"post","link":"https:\/\/eurecat.org\/en\/digital-platform-to-be-developed-to-process-large-scale-omics-datasets-using-big-data-and-artificial-intelligence\/","title":{"rendered":"Digital platform to be developed to process large-scale omics datasets using big data and artificial intelligence"},"content":{"rendered":"

The\u00a0Eurecat<\/strong><\/a>\u00a0technology centre is coordinating the European\u00a0Glomicave<\/strong><\/a> project which is to develop a new digital platform which can process large-scale\u00a0omics datasets<\/strong>\u00a0using\u00a0Big Data<\/strong>\u00a0and\u00a0Artificial Intelligence<\/strong> by leveraging pre-existing data to enhance our understanding of biological systems as a whole.<\/p>\n

The project, which has funding coming to \u20ac6,372,583, addresses the need to build systems which can relate genotypes, i.e. the genetic content of an organism, with phenotypes, which are the visible characteristics of the organism resulting from the interaction between its genotype and the environment, by integrating experimental omics datasets with data available in public repositories and scientific texts.<\/p>\n

The project \u201cwill provide scientific and industrial experts and non-experts alike with a tool which will help them to pinpoint and understand new links between animal, plant and environmental genotypes and phenotypes,\u201d says Glomicave project coordinator Biotza Gutierrez, public programme manager at the Eurecat technology centre.<\/p>\n

\u201cMulti-omics integration enriched with automated extraction and interpretation of knowledge in scientific texts will enhance our understanding of biological systems and enable more accurate genotypic-phenotypic associations while streamlining the experimental design of new studies,\u201d argues N\u00faria Canela, director of the Omics Sciences Unit, a joint R&D division made up of specialists from Eurecat and Rovira i Virgili University (URV).<\/p>\n

In Glomicave, \u201cthe scientific literature, made by humans for humans, will be transformed into a computational knowledge base, i.e. a knowledge graph,\u201d she adds. \u201cThis will allow users to query operations to find new hidden genotype-phenotype links not explicitly described in the literature and also to integrate this information with new experimental datasets in scientific studies.\u201d<\/p>\n

To this end, data mining strategies will be used to compile and extract information from the scientific literature coupled with natural language processing (NLP) to interpret the information and integrate it into the knowledge base. State-of-the-art information extraction methods for biomedical text will be adapted and extended to create and populate existing knowledge graphs representing biological entities and concepts. The focus will be on methods which deliver interpretable and verifiable extractions.<\/p>\n

The platform driven by the Glomicave project will be validated in the livestock, agro-biotechnology and environmental sectors. It will address specific challenges in six business cases related to animal breeding technology, meat quality, fruit growth and quality, plant growth, phosphorus removal and recovery, and bioenergy production in the urban water cycle.<\/p>\n

The Glomicave project is funded by the European Union\u2019s Horizon 2020 programme and has a consortium made up of 15 partners from Spain, France, Germany, Portugal, Belgium and Denmark. They consist of four technology and research centres in Eurecat, the project coordinator, SERIDA, INRAE and Forschungszentrum Julich; three universities in Aalborg University, the University of Minho and Katholieke Universiteit Leuven; four SMEs in ASINCAR, TREE Technology, Allice and AkiNaO; two large corporations in NEC Laboratories Europe and Aguas do Norte; the ASEAVA animal cluster, and UNE as a standardisation body.<\/p>\n","protected":false},"excerpt":{"rendered":"

The\u00a0Eurecat\u00a0technology centre is coordinating the European\u00a0Glomicave project which is to develop a new digital platform which can process large-scale\u00a0omics datasets\u00a0using\u00a0Big Data\u00a0and\u00a0Artificial Intelligence by leveraging pre-existing data to enhance our understanding of biological systems as a whole. The project, which has funding coming to \u20ac6,372,583, addresses the need to build systems which can relate genotypes, i.e. […]<\/p>\n","protected":false},"author":27,"featured_media":116090,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[415,417,416,743,432,433],"tags":[],"class_list":["post-125108","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-big-data-en","category-consulting","category-omics-sciences","category-press-notes-en","category-smart-management-systems-en","category-sustainability"],"yoast_head":"\nDigital platform to be developed to process large-scale omics datasets using big data and artificial intelligence - Eurecat<\/title>\n<meta name=\"description\" content=\"Digital platform to be developed to process large-scale omics datasets using big data and artificial intelligence\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/eurecat.org\/en\/digital-platform-to-be-developed-to-process-large-scale-omics-datasets-using-big-data-and-artificial-intelligence\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Digital platform to be developed to process large-scale omics datasets using big data and artificial intelligence - 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