MACOED

The software tool MACOED proposes a multi-objective heuristic optimization methodology for detecting genetic interactions in genome-wide association studies. MACOED combines logistical regression and Bayesian network methods to improve detection power and reduce false-positive rates. The tool uses a memory-based multi-objective ant colony optimization algorithm to handle high-dimensional problems. The method outperforms other recent algorithms in both detection power and computational feasibility for large datasets, according to experimental results.

Topic

Molecular interactions, pathways and networks;Genetics

Detail

  • Operation: Optimisation and refinement

  • Software interface: Command-line user interface

  • Language: MATLAB;C++

  • License: -

  • Cost: Free

  • Version name: -

  • Credit: The National Natural Science Foundation of China, Shanghai Science and Technology Commission, Foundation for the Author of National Excellent Doctoral Dissertation of PR China

  • Input: -

  • Output: -

  • Contact: Pengjie Jing jingse@sjtu.edu.cn, Hongbin Shen hbshen@sjtu.edu.cn

  • Collection: -

  • Maturity: -

Publications

  • MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies.
  • Jing PJ and Shen HB. MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies. MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies. 2015; 31:634-41. doi: 10.1093/bioinformatics/btu702
  • https://doi.org/10.1093/bioinformatics/btu702
  • PMID: 25338719
  • PMC: -

Download and documentation

< Back to DB search