57- https://doi.org/10.1109/tcbb.2015.2474384
Hybrid Framework Using Multiple-Filters and an Embedded Approach for an Efficient Selection and Classification of Microarray Data.
- Aug 28, 2015
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Edmundo Bonilla-Huerta +3 more
A hybrid framework composed of two stages for gene selection and classification of DNA microarray data is proposed. At the first stage, five traditional statistical methods are combined for preliminary gene selection (Multiple Fusion Filter). Then, different relevant gene subsets are selected by using an embedded Genetic Algorithm (GA), Tabu Search (TS), and Support Vector Machine (SVM). A gene subset, consisting of the most relevant genes, is obtained from this process, by analyzing the frequency of each gene in the different gene subsets. Finally, the most frequent genes are evaluated by the embedded approach to obtain a final relevant small gene subset with high performance. The proposed method is tested in four DNA microarray datasets. From simulation study, it is observed that the proposed approach works better than other methods reported in the literature.
- # Classification Of Microarray Data
- # Relevant Gene Subset
- # Classification Of DNA Microarray Data
- # Relevant Gene
- # Gene Subset
- # DNA Microarray Datasets
- # Traditional Statistical Methods
- # Frequent Genes
- # Approach For Classification Of Data
- # Efficient Classification Of Data
