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Published on: October 4, 2019
Classification of cancer types based on microRNA expression using a hybrid radial basis function and particle swarm
Masoumeh Soleimani1, Aryan Harooni2, Nasim Erfani3
1Department of Mathematics and Statistical Sciences, Clemson University, Clemson, South Carolina, USA.
Abstract:
The diagnosis and treatment of cancer is one of the most challenging aspects of the medical profession, despite advances in disease diagnosis. MicroRNAs are small noncoding RNA molecules involved in regulating gene expression and are associated with several cancer types. Therefore, the analysis of microRNA data has become one of the most important areas of cancer research in recent years. This paper presents an improved method for cancer-type classification based on microRNA expression data using a hybrid radial basis function (RBF) and particle swarm optimization (PSO) algorithm. Two datasets containing microRNA information were used, and preprocessing and normalization operations were performed on the raw data. Feature selection was carried out by using the PSO algorithm, which can identify the most relevant and informative features in the data along with helping to prioritize them. Using a PSO algorithm for feature selection is an effective approach to microRNA analysis. This enhances the accuracy and reliability of cancer-type classifications based on microRNA expression data. In the proposed method, we, respectively, achieved an accuracy of 0.95% and 0.91% on both datasets, with an average of 0.93%, using an improved RBF neural network classifier. These results demonstrate that the proposed method outperforms previous works. RESEARCH HIGHLIGHTS: To enhance the accuracy of cancer-type classifications based on microRNA expression data. We present a minimal feature selection method using particle swarm optimization to reduce computational load & radial basis function to improve accuracy.
Insights
This study introduces an improved method for cancer-type classification using microRNA expression data. The hybrid radial basis function and particle swarm optimization algorithm enhances accuracy and reliability in identifying cancer types.
Area of Science:
- Biotechnology
- Bioinformatics
- Genomics
Background:
- MicroRNAs (miRNAs) are crucial regulators of gene expression implicated in various cancers.
- Accurate cancer-type classification from miRNA expression data is vital for effective diagnosis and treatment.
- Current methods face challenges in accuracy and computational efficiency.
Purpose of the Study:
- To develop an improved computational method for accurate cancer-type classification using miRNA expression data.
- To enhance classification accuracy and reduce computational load through optimized feature selection.
Main Methods:
- A hybrid approach combining radial basis function (RBF) neural networks with particle swarm optimization (PSO) was employed.
- PSO was utilized for efficient feature selection to identify the most relevant miRNA biomarkers.
- Data preprocessing and normalization were performed on two distinct miRNA expression datasets.
Main Results:
- The proposed method achieved high classification accuracies of 95% and 91% on the two datasets.
- An average accuracy of 93% was obtained, demonstrating superior performance compared to existing methods.
- PSO effectively reduced the feature set, contributing to computational efficiency.
Conclusions:
- The hybrid RBF-PSO method offers a robust and accurate approach for cancer-type classification based on miRNA expression.
- This technique holds significant potential for advancing cancer diagnostics and personalized medicine.
- Minimal feature selection via PSO combined with RBF classification improves reliability and efficiency.
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