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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 biomedical literature holds our understanding of pharmacogenomics, but it is dispersed across many journals. In order to integrate our knowledge, connect important facts across publications and generate new hypotheses we must organize and encode the contents of the literature. By creating databases of structured pharmocogenomic knowledge, we can make(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)
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)
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)
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)
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)