Blog: Data Mining Algorithm Explains Complex Temporal Interactions Among Genes

Data Mining Algorithm Explains Complex Temporal Interactions Among Genes
Virginia Tech News (06/22/10) Trulove, Susan

Researchers at Virginia Tech (VT), New York University (NYU), and the University of Milan have developed Gene Ontology based Algorithmic Logic and Invariant Extractor (GOALIE), a data-mining algorithm that can automatically reveal how biological processes are coordinated in time. GOALIE reconstructs temporal models of cellular processes from gene expression data. The researchers developed and applied the algorithm to time-course gene expression datasets from budding yeast. "A key goal of GOALIE is to be able to computationally integrate data from distinct stress experiments even when the experiments had been conducted independently," says VT professor Naren Ramakrishnan. NYU professor Bud Mishra notes GOALIE also can extract entire formal models that can then be used for posing biological questions and reasoning about hypotheses. The researchers hope the tool can be used to study disease progression, aging, host-pathogen interactions, stress responses, and cell-to-cell communication.

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