Objective
Scientific publications are the main media through which researchers report their new findings. The huge amount and the continuing rapid growth of the number of published articles in biomedicine, has made it particularly difficult for researchers to access and utilize the knowledge contained in them. Currently, there are over 21 million publications indexed in PubMed, which is the main system that provides access to the biomedical literature. Over 2000 new entries are added to the system every day. Developing text mining techniques to automatically extract biologically important information such as relationships between biomolecules is not only useful, but also necessary to facilitate biomedical research and to speed-up scientific progress. Most of the prior studies in the biomedical text mining field tackle the problem of extracting the fact that there is a relationship between a pair of biomolecules. However, for extracted information to make sense, a great deal of biological context is required. While some of this context such as relationship type and directionality is found in the sentence that actually reports the relationship, some of it such as species and experimental method is likely stated elsewhere in the article. The goal of the proposed project is to design methods based on natural language processing and machine learning to extract relationships among biomolecules and their local (sentence-level) and non-local (document-level) context information, as well as to design novel knowledge discovery methods that utilize the extracted contextual information
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences biological sciences biochemistry biomolecules
- natural sciences computer and information sciences data science natural language processing
- natural sciences computer and information sciences artificial intelligence machine learning
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP7-PEOPLE-2011-CIG
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MC-CIG - Support for training and career development of researcher (CIG)
Coordinator
34342 Istanbul
Türkiye
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.