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Content archived on 2024-05-29

Cross-Study Analysis of Cancer Gene Expression Datasets


Data from an increasing number of high-throughput cancer gene expression studies are becoming available. Few researchers, however, have attempted to integrate these data. We propose to developbioinformatic tools for the cross-study analysis of expression data. We will use them
(i) to cross-validate thyroid cancer datasets,
(ii) to compare expression in micro dissected tumours and in their cell line counterparts, and (iii) to find shared characteristics between different types of cancers. In addition, theories respond to the need for reuse and integration of gene expression data flowing at an increasing rate into public databases. The applicant was trained in computer and cognitive sciences. His research experience in computer and mathematical modelling in biology led to papers in respected journals, including two PNAS publications. With a history of landmark discoveries and an average of 100 publications per year, the host, IRIBHM, Free University of Brussels, is a leading European lab in thyroid cancer. Its microarray group has been the first to do thyroid cancer microarray in Europe. The applicant has spent his last five research years in the United States and wish to return to Europe.IRIBHM has the critical mass of people and collaborations needed for a successful integration in European research. The pesto will complement his biomathematics experience with expertise in high-throughput data analysis. This skill is an essential step toward the applicant's goal of moving to medical applications. Joining a wet lab after years in computational biology institutions will also extend his biological knowledge.

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