Background: Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant - drug - adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug.
View Article and Find Full Text PDFUntargeted metabolomics is a powerful phenotyping tool for better understanding biological mechanisms involved in human pathology development and identifying early predictive biomarkers. This approach, based on multiple analytical platforms, such as mass spectrometry (MS), chemometrics and bioinformatics, generates massive and complex data that need appropriate analyses to extract the biologically meaningful information. Despite various tools available, it is still a challenge to handle such large and noisy datasets with limited number of individuals without risking overfitting.
View Article and Find Full Text PDFThis study is dedicated to the introduction of a novel method that automatically extracts potential structural alerts from a data set of molecules. These triggering structures can be further used for knowledge discovery and classification purposes. Computation of the structural alerts results from an implementation of a sophisticated workflow that integrates a graph mining tool guided by growth rate and stability.
View Article and Find Full Text PDFFunctional classification aims at grouping genes according to their molecular function or the biological process they participate in. Evaluating the validity of such unsupervised gene classification remains a challenge given the variety of distance measures and classification algorithms that can be used. We evaluate here functional classification of genes with the help of reference sets: KEGG (Kyoto Encyclopaedia of Genes and Genomes) pathways and Pfam clans.
View Article and Find Full Text PDFOne current challenge in biomedicine is to analyze large amounts of complex biological data for extracting domain knowledge. This work holds on the use of knowledge-based techniques such as knowledge discovery (KD) and knowledge representation (KR) in pharmacogenomics, where knowledge units represent genotype-phenotype relationships in the context of a given treatment. An objective is to design knowledge base (KB, here also mentioned as an ontology) and then to use it in the KD process itself.
View Article and Find Full Text PDFBackground: The Gene Ontology (GO) is a well known controlled vocabulary describing the biological process, molecular function and cellular component aspects of gene annotation. It has become a widely used knowledge source in bioinformatics for annotating genes and measuring their semantic similarity. These measures generally involve the GO graph structure, the information content of GO aspects, or a combination of both.
View Article and Find Full Text PDFThe formability of a bond in a target molecule is a bond property related to the problem of finding a reaction that synthesizes the target by forming the bond: the easier this problem, the higher the formability. Bond formability provides an interesting piece of information that might be used for selecting strategic bonds during a retrosynthesic analysis or for assessing synthetic accessibility in virtual screening. The article describes a graph-mining algorithm called GemsBond that evaluates formability of bonds by mining structural environments contained in several thousand molecular graphs of reaction products.
View Article and Find Full Text PDFBackground: 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 Web technologies concerning the understanding and availability of tools for knowledge representation, extraction, and reasoning.
Results: This paper presents a method that exploits bio-ontologies for guiding data selection within the preparation step of the KDD process.
In France, cancer care is evolving to the design of regional networks, so as to coordinate expertise, services and resources allocation. Existing information systems along with data-mining tools can provide better knowledge on the distribution of patient flows. We used one year data of the French Diagnosis Related Groups (DRGs) based system to perform our analysis.
View Article and Find Full Text PDFStud Health Technol Inform
December 2004
This paper presents the KASIMIR research project for the management of decision protocols in oncology. A decision protocol is a kind of decision tree implemented in an object-based representation formalism. A reasoner based on such a formalism and on hierarchical classification is coupled with a knowledge editor.
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