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ANN Based Features Selection Approach Using Hybrid GA-PSO for siRNA Design


Affiliations
1 Department of Computer Science, Assam University Silchar, Assam, India
2 Department of Agril. Biotechnology, Assam Agricultural University, Assam, India
 

siRNA has become an indispensible tool for silencing gene expression. It can act as an antiviral agent in RNAi pathway against plant diseases caused by plant viruses. However, identification of appropriate features for effective siRNA design has become a pressing issue for researchers which need to be resolved. Feature selection is a vital pre-processing technique involved in bioinformatics data set to find the most discriminative information not only for dimensionality reduction and detection of relevance features but also for minimizing the cost associated with features to design an accurate learning system. In this paper, we propose an ANN based feature selection approach using hybrid GA-PSO for selecting feature subset by discarding the irrelevant features and evaluating the cost of the model training. The results showed that the performance of proposed hybrid GA-PSO model outperformed the results of general PSO.

Keywords

SIRNA, PSO, GA-PSO, Features Selection, ANN, Cost Evaluation, GA-BPNN, Heuristic Optimization.
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  • ANN Based Features Selection Approach Using Hybrid GA-PSO for siRNA Design

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Authors

Ranjan Sarmah
Department of Computer Science, Assam University Silchar, Assam, India
Mahendra K. Modi
Department of Agril. Biotechnology, Assam Agricultural University, Assam, India
Shahin Ara Begum
Department of Computer Science, Assam University Silchar, Assam, India

Abstract


siRNA has become an indispensible tool for silencing gene expression. It can act as an antiviral agent in RNAi pathway against plant diseases caused by plant viruses. However, identification of appropriate features for effective siRNA design has become a pressing issue for researchers which need to be resolved. Feature selection is a vital pre-processing technique involved in bioinformatics data set to find the most discriminative information not only for dimensionality reduction and detection of relevance features but also for minimizing the cost associated with features to design an accurate learning system. In this paper, we propose an ANN based feature selection approach using hybrid GA-PSO for selecting feature subset by discarding the irrelevant features and evaluating the cost of the model training. The results showed that the performance of proposed hybrid GA-PSO model outperformed the results of general PSO.

Keywords


SIRNA, PSO, GA-PSO, Features Selection, ANN, Cost Evaluation, GA-BPNN, Heuristic Optimization.

References