This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Multiclass Gene Selection Using Pareto-Fronts
Jan.-Feb. 2013 (vol. 10 no. 1)
pp. 87-97
Jagath C. Rajapakse, Sch. of Comput. Eng., Nanyang Technol. Univ., Singapore, Singapore
Piyushkumar A. Mundra, Bioinf. Res. Center, Nanyang Technol. Univ., Singapore, Singapore
Filter methods are often used for selection of genes in multiclass sample classification by using microarray data. Such techniques usually tend to bias toward a few classes that are easily distinguishable from other classes due to imbalances of strong features and sample sizes of different classes. It could therefore lead to selection of redundant genes while missing the relevant genes, leading to poor classification of tissue samples. In this manuscript, we propose to decompose multiclass ranking statistics into class-specific statistics and then use Pareto-front analysis for selection of genes. This alleviates the bias induced by class intrinsic characteristics of dominating classes. The use of Pareto-front analysis is demonstrated on two filter criteria commonly used for gene selection: F-score and KW-score. A significant improvement in classification performance and reduction in redundancy among top-ranked genes were achieved in experiments with both synthetic and real-benchmark data sets.
Index Terms:
Gene expression,Bioinformatics,Computational biology,Redundancy,Cancer,Training,Benchmark testing,Pareto-front analysis,Aggregation statistics,filter methods,gene selection,multiobjective evolutionary optimization
Citation:
Jagath C. Rajapakse, Piyushkumar A. Mundra, "Multiclass Gene Selection Using Pareto-Fronts," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 10, no. 1, pp. 87-97, Jan.-Feb. 2013, doi:10.1109/TCBB.2013.1
Usage of this product signifies your acceptance of the Terms of Use.