Marie-Dominique Devignes

Suggested ontology for pharmacogenomics (SO-Pharm): modular construction and preliminary testing

Pharmacogenomics studies the involvement of interindividual variations of DNA sequence in different drug responses (especially adverse drug reactions) Knowledge Discovery in Databases (KDD) process is a means for discovering new pharmacogenomic knowledge in biological databases However data complexity makes it necessary to guide the KDD process by representation of domain knowledge Three domains at least are in concern: genotype, drug and phenotype The approach described here aims at reusing whenever possible existing domain knowledge in order to build a modular formal representation of domain knowledge in pharmacogenomics The resulting ontology is called SO-Pharm for Suggested Ontology for Pharmacogenomics Various situations encountered during the construction process are analyzed and discussed A preliminary validation is provided by representing with SO-Pharm concepts some well-known examples of pharmacogenomic knowledge.

Ontology-guided data preparation for discovering genotype-phenotype relationships

BackgroundComplexity 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 Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning.ResultsThis paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process. We propose three scenarios in which domain knowledge and ontology elements such as subsumption, properties, class descriptions, are taken into account for data selection, before the data mining step. Each of these scenarios is illustrated within a case-study relative to the search of genotype-phenotype relationships in a familial hypercholesterolemia dataset. The guiding of data selection based on domain knowledge is analysed and shows a direct influence on the volume and significance of the data mining results.ConclusionsThe method proposed in this paper is an efficient alternative to numerical methods for data selection based on domain knowledge. In turn, the results of this study may be reused in ontology modelling and data integration.

SNP-Converter: an ontology-based solution to reconcile heterogeneous SNP descriptions for pharmacogenomic studies

Pharmacogenomics explores the impact of individual genomic variations in health problems such as adverse drug reactions. Records of millions of genomic variations, mostly known as Single Nucleotide Polymorphisms (SNP), are available today in various overlapping and heterogeneous databases. Selecting and extracting from these databases or from private sources a proper set of polymorphisms are the first steps of a KDD (Knowledge Discovery in Databases) process in pharmacogenomics. It is however a tedious task hampered by the heterogeneity of SNP nomenclatures and annotations. Standards for representing genomic variants have been proposed by the Human Genome Variation Society (HGVS). The SNP-Converter application is aimed at converting any SNP description into an HGVS-compliant pivot description and vice versa. Used in the frame of a knowledge system, the SNP-Converter application contributes as a wrapper to semantic data integration and enrichment.

Using Domain Knowledge to Guide Lattice-based Complex Data Exploration

In this paper we propose an approach which combines semantic resources and formal concept analysis to deal with heterogenous data sets represented as many-valued (MV) formal contexts. We define a new Galois connection considering the semantic relationships between attribute values in a MV context. The semantic relationships are used to calculate the similarity between attribute values to decide whether an attribute is shared by a set of objects or not. Then, based on this Galois connection, we define MV formal concepts and MV concept lattices. Depending on a chosen similarity threshold, MV concept lattices may have different levels of precision. We take advantage of this feature to browse the content of a biological databases repository in a dynamic and progressive way. The browsing process combines the navigation in several MV concept lattices and allows zooming operations by switching between MV concept lattices with higher or lower precision.

Extending Attribute Dependencies for Lattice-Based Querying and Navigation

In this paper we study dependencies of attributes in the context of Formal Concept Analysis. These dependencies allow to define a hierarchy of attributes reflecting the importance or interest in attributes. A hierarchy of attributes is a set of attributes partially ordered with respect to their importance. It represents domain knowledge used to improve lattice-based querying and navigation. Actually, in lattice-based querying, hierarchies of attributes are used to define complex queries containing attributes with different levels of importance: more important attributes define the focus of the retrieval while less important ones define secondary information whose presence is desirable in the answers. Furthermore, the relation between attributes in a complex query represents implicit or explicit knowledge units that must be considered while computing answers. Similarly, in lattice-based navigation, the choice of moving to a particular concept rather than to another is influenced by the higher importance of the attributes in the concept intent. Hence, the design and use of a hierarchy of attributes leads to a navigation guided by domain knowledge.