- Poster presentation
- Open Access
Context-sensitive use of bioinformatics tools with complementary functionalities for generation of relevant hypothesis
© Abedi et al; licensee BioMed Central Ltd. 2014
- Published: 29 September 2014
- Gene Ontology
- Bioinformatics Tool
- Complementary Functionality
- Gene Ontology Classification
- Data Reuse
Bioinformatics tools can be of great help in mining and summarizing voluminous data. However, each tool has a limited array of functionalities and is targeted for niche users. Integration of bioinformatics tools with complementary functionalities, designed on different data types, can potentially enhance user experience and further knowledge discovery. We have developed a progressive approach to integrate bioinformatics tools by examining the diversity of tools that infer complementary information from the literature, high throughput genomic data and the human curated Gene Ontology classification. The goal is to build tools for inferring new and refined hypotheses for complex diseases and guide researchers towards the most fruitful directions in designing experiments and collaborating in interdisciplinary research.
This pilot study provides a systemic approach to explore complex diseases using an array of bioinformatics tools. Such study could lead to tool integration. As a proof of concept, Alzheimer’s disease (AD) was explored, and an indirect association between AD and tuberculosis was identified. Matrix metalloproteinases genes and their mode of action are the origin for this association.
Integration of complementary tools can help to combine functionalities and broaden services to an increasingly interdisciplinary field. The integrated system will assist the human expert and will bring hidden associations, promote data reuse, and stimulate interdisciplinary projects by connecting information across the disciplines. This may also further multi-faceted issues in knowledge discovery.
This work was supported by the Electrical and Computer Engineering Department at the University of Memphis, the University of Tennessee Health Science Center, and by NSF grant NSF-IIS-0746790.
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