Linking Life Sciences Data Using Graph-Based Mapping
DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
DILS '09 Proceedings of the 6th International Workshop on Data Integration in the Life Sciences
An integrated QAP-based approach to visualize patterns of gene expression similarity
ACAL'07 Proceedings of the 3rd Australian conference on Progress in artificial life
Fast and accurate estimation of shortest paths in large graphs
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A data warehouse approach to semantic integration of pseudomonas data
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
Variation of background knowledge in an industrial application of ILP
ILP'10 Proceedings of the 20th international conference on Inductive logic programming
Biomine: a network-structured resource of biological entities for link prediction
Bisociative Knowledge Discovery
Modelling a biological system: network creation by triplet extraction from biological literature
Bisociative Knowledge Discovery
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
Guiding the interactive exploration of metabolic pathway interconnections
Information Visualization
Hi-index | 3.84 |
Motivation: Assembling the relevant information needed to interpret the output from high-throughput, genome scale, experiments such as gene expression microarrays is challenging. Analysis reveals genes that show statistically significant changes in expression levels, but more information is needed to determine their biological relevance. The challenge is to bring these genes together with biological information distributed across hundreds of databases or buried in the scientific literature (millions of articles). Software tools are needed to automate this task which at present is labor-intensive and requires considerable informatics and biological expertise. Results: This article describes ONDEX and how it can be applied to the task of interpreting gene expression results. ONDEX is a database system that combines the features of semantic database integration and text mining with methods for graph-based analysis. An overview of the ONDEX system is presented, concentrating on recently developed features for graph-based analysis and visualization. A case study is used to show how ONDEX can help to identify causal relationships between stress response genes and metabolic pathways from gene expression data. ONDEX also discovered functional annotations for most of the genes that emerged as significant in the microarray experiment, but were previously of unknown function. Availability: ONDEX is freely available under the GPL License and can be downloaded from SourceForge http://ondex.sourceforge.net/ Contact: Jacob.Koehler@bbsrc.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.