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KuangR

Research Abstracts Online
January - December 2011

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University of Minnesota Twin Cities
College of Science and Engineering
Department of Computer Science and Engineering

PI: Rui Kuang

Network Learning for Integrative Cancer Genomics

Next-generation DNA sequencing along with other high-throughput biotechnologies now allows mapping of the complete DNA sequences and other molecular features of a cancer genome. The crucial next step is to interpret and model the huge amount of genome information for scientific discoveries and improved clinic decisions for a more personalized treatment of cancer. The objective of this research is to formulate novel network-based machine learning methods and theoretical frameworks that can model the underlying biological mechanisms for an integrative study of cancer patient genomes and relevant biomedical knowledge. The scientific challenges are: How do we formulate predictive models that can integrate various types of cancer genome aberrations to improve outcome predictions for patient diagnosis? And how do we elucidate the relations among cancers, genes and the genome aberrations that can disrupt gene functions and biological pathways, for identifying potential drug targets? As a proof of concept, the developed methods will be applied to integrate heterogenous patient genomic data for studying chemo-resistance in the treatment of ovarian cancer and development of lung cancer under the collaborations with two principle investigators at Mayo Clinic.

Group Members

Jeremy Chien, Laboratory Medicine and Pathology, Mayo Clinic School of Medicine, Rochester, Minnesota
Changjin Hong, Graduate Student
Taehyun Hwang, Graduate Student
Ze Tian, Graduate Student
Wei Zhang, Graduate Student