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Most pharmacogenomics knowledge is contained in the text of published studies, and is thus not available for automated computation. Natural Language Processing (NLP) techniques for extracting relationships in specific domains often rely on hand-built rules and domain-specific ontologies to achieve good performance. In a new and evolving field such as(More)
The volume of publicly available data in biomedicine is constantly increasing. However, these data are stored in different formats and on different platforms. Integrating these data will enable us to facilitate the pace of medical discoveries by providing scientists with a unified view of this diverse information. Under the auspices of the National Center(More)
Identifying functions shared by genes responsible for cancer is a challenging task. This paper describes the preparation work for applying Formal Concept Analysis (FCA) to complex biological data. We present here a preliminary experiment using these data on a core context with the addition of domain knowledge. The resulting concept lattices are explored and(More)
Pharmacogenomics studies the involvement of interindivid-ual variations in DNA sequence into different drug responses (especially adverse drug reactions). Knowledge Discovery in Databases (KDD) process is a means for discovering new pharmacogenomic knowledge units in biological databases. However data complexity makes it necessary to guide the KDD process(More)
BACKGROUND Complexity and amount of post-genomic data constitute two major factors limiting the application of Knowledge Discovery in Databases (KDD) methods in life sciences. Bio-ontologies may nowadays play key roles in knowledge discovery in life science providing semantics to data and to extracted units, by taking advantage of the progress of Semantic(More)
Annotating data with concepts of an ontology is a common practice in the biomedical domain. Resulting annotations, i.e., data-concept relationships, are useful for data integration whereas the reference ontology can guide the analysis of integrated data. Then the analysis of annotations can provide relevant knowledge units to consider for extracting and(More)
BACKGROUND Advances in Natural Language Processing (NLP) techniques enable the extraction of fine-grained relationships mentioned in biomedical text. The variability and the complexity of natural language in expressing similar relationships causes the extracted relationships to be highly heterogeneous, which makes the construction of knowledge bases(More)
We propose an approach for extending domain knowledge represented in DL ontology by using knowledge extraction methods on ontology assertions. Concept and role assertions are extracted from the ontology in the form of assertion graphs, which are used to generate a formal context manipulated by Formal Concept Analysis methods. The resulting expressions are(More)
Advances in concept recognition and natural language parsing have led to the development of various tools that enable the identification of biomedical entities and relationships between them in text. The aim of the Genotype-Phenotype-Drug Relationship Extraction from Text workshop (or GPD-Rx workshop) is to examine the current state of art and discuss the(More)