Gene expression profiling allows clinical diagnosis to be made on a molecular level, thereby substantially increasing diagnosis accuracy and facilitating choice of treatment based on the patients’ genetic traits. Moreover, identifying disease-related genes and monitoring their activity levels provide insights into disease mechanisms. We aim improve methods for disease diagnosis and marker selection by integrating additional sources of data and the design of methods capable of handling high dimensional spaces. Moreover, next-generation sequencing technologies allows the inference of genome structural variations for individual levels. We are interested in developing methods for accurate detection of variants and use of predictions in association studies.
Ivan G Costa
Redestig, H., Costa, I. G. Detection and interpretation of metabolite-transcript co-responses using combined profiling data.Bioinformatics (Proceedings of ISMB 2011), v. 27(13), p. i357-i365, 2011. [paper].[Supp]
I. G. Costa, R. Krause, L. Optiz, and A. Schliep Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data. BMC Bioinformatics 2007, Vol. 8, Pages S3. [paper] [Supp].