Myra Samuels Memorial Lecture

Exploring complex interactions in genomic data for aiding the translational medicine research

Professor Ker-Chau Li
UCLA and Institute of Statistical Science, Academia Sinica

Start Date and Time: Thu, 8 Apr 2010, 4:30 PM

End Date and Time: Thu, 8 Apr 2010, 5:30 PM

Venue: MATH 175

Abstract:

In recent years, we have witnessed the astonishing growth in the public repertoire of biological data and knowledge resource. This includes the completion of genome sequencing for human and many species, the stride in the SNP detection and international HapMap project, the accumulation of full genome microarray gene expression data under a number of conditions for numerous organisms and tissues, the identification of the high density genetic markers, protein-protein interaction and complexes, as well as the availability of various gene annotation websites featuring both functional and structural information of the gene products, their biological roles and relevance to disease studies. Such open sources hold the promise of benefiting numerous projects aiming at solving detail genetic profiles predisposing to complex diseases and their trait components, and they would be of great use for translational medicine research.

In this talk, I will present an integrative framework of bio-data computing that is developed in my lab, featuring the approach of liquid association (LA) for exploring complex patterns of interaction between various levels of genomic, phenotypic, clinical or environmental variables. This approach generally overcomes the deficiencies found in existing equipment, systems and methods for failing to exploit structures of “uncorrelatedness” between variables. This is a great competitive edge over other approaches. Illustrative examples ranging from the elucidation of gene regulation patterns in metabolic pathways to the search of candidate genes for complex diseases will be given. I will also report some preliminary findings from an exploration of copy number variation data for lung cancer patients with different EGFR mutation statuses in Taiwan. The results may shed light on personalized therapy for lung cancer management.