Fatima Al-Shahrour | PubMed Health

PubMed2006

Ontology-driven approaches to analyzing data in functional genomics.

Ontologies are fundamental knowledge representations that provide not only standards for annotating and indexing biological information, but also the basis for implementing functional classification and interpretation models. This chapter discusses the application of gene ontology (GO) for predictive tasks in functional genomics. It focuses on the problem of analyzing functional patterns associated with gene products. This chapter is divided into two main parts. The first part overviews GO and its applications for the development of functional classification models. The second part presents two methods for the characterization of genomic information using GO. It discusses methods for measuring functional similarity of gene products, and a tool for supporting gene expression clustering analysis and validation.

Cluster Analysis↗

PubMed2004

Expression profiling of T-cell lymphomas differentiates peripheral and lymphoblastic lymphomas and defines survival related genes.

PURPOSE: T-Cell lymphomas constitute heterogeneous and aggressive tumors in which pathogenic alterations remain largely unknown. Expression profiling has demonstrated to be a useful tool for molecular classification of tumors. EXPERIMENTAL DESIGN: Using DNA microarrays (CNIO-OncoChip) containing 6386 cancer-related genes, we established the expression profiling of T-cell lymphomas and compared them to normal lymphocytes and lymph nodes. RESULTS: We found significant differences between the peripheral and lymphoblastic T-cell lymphomas, which include a deregulation of nuclear factor-kappaB signaling pathway. We also identify differentially expressed genes between peripheral T-cell lymphoma tumors and normal T lymphocytes or reactive lymph nodes, which could represent candidate tumor markers of these lymphomas. Additionally, a close relationship between genes associated to survival and those that differentiate among the stages of disease and responses to therapy was found. CONCLUSIONS: Our results reflect the value of gene expression profiling to gain insight about the molecular alterations involved in the pathogenesis of T-cell lymphomas.

Cell Survival↗