Incorporating Ontology-Driven Similarity Knowledge into Functional Genomics: An Exploratory Study
BIBE '04 Proceedings of the 4th IEEE Symposium on Bioinformatics and Bioengineering
Rule-based workflow management for bioinformatics
The VLDB Journal — The International Journal on Very Large Data Bases
Correlation between Gene Expression and GO Semantic Similarity
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Building a disordered protein database: a case study in managing biological data
ADC '07 Proceedings of the eighteenth conference on Australasian database - Volume 63
So-Grid: A self-organizing Grid featuring bio-inspired algorithms
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Modelling concepts and database implementation techniques for complex biological data
International Journal of Bioinformatics Research and Applications
International Journal of Bioinformatics Research and Applications
Biomedical ontology improves biomedical literature clustering performance: a comparison study
International Journal of Bioinformatics Research and Applications
Dynamic algorithm for inferring qualitative models of Gene Regulatory Networks
International Journal of Data Mining and Bioinformatics
Modelling of the mitochondrial apoptosis network
International Journal of Bioinformatics Research and Applications
Bioinformatics
Bioinformatics
Bioinformatics
Applications of artificial intelligence in bioinformatics: A review
Expert Systems with Applications: An International Journal
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The wealth of available datasets in genomics and proteomics has spawned a corresponding interest in knowledge discovery methods that enable researchers to attain substantial biological meanings pertaining to model various human diseases. The scale and complexity of these datasets give rise to important challenges in data management and analysis. This paper aims at designing a framework that helps researchers to better conduct data analysis through seamless biological data integration from various literature and online databases. A rule-based program is designed and implemented to access the GO database and retrieve datasets related to apoptosis. An advanced relational learning method is employed to infer causal relationships between the retrieved datasets. As a result, this framework aims at providing a level of query processing beyond even some high-profile websites. This will lead to an improved understanding of the pathogenesis of a certain disease and eventually to its therapy. In addition, this work also provides a comprehensive picture of biological processes, gene functions and protein properties that may detect disease patterns and lead to novel disease concepts.