Optimization and Metaheuristic Methods for Cancer Research

Partner Investigator:
Michael A. Langston, Department of Computer Science, University of Tennessee
Abstract:
This project is designed to provide novel mathematical models and advanced non-numerical algorithms for biological data analysis. A major goal is to devise powerful new pattern recognition and molecular diagnostic software for colorectal, breast, prostate, melanoma, lung and other high-incidence cancers in Australia. Specific aims include
(1) the use of combinatorial optimization modeling to help understand the foundational complexity of underlying problems in both the classical and the parameterized settings,
(2) the application of state-of-the-art methods for the synthesis of pre-processing rules for efficient extremal structures to aid genomic data analysis,
(3) the development of hybrid algorithmic solutions based on a combination of fixed-parameter tractable algorithms, integer programming and evolutionary computation methods, and
(4) the study of how these innovative solutions can help answer questions in genetics related to the analysis of gene expression and single nucleotide polymorphisms in cancer research data.
Participants:
The Chief Investigator for this project is Pablo Moscato at The University of Newcastle, New South Wales, Australia.
The other Partner Investigator is Rodney J. Scott at The University of Newcastle, New South Wales, Australia.