scholarly journals ABC-SVM: Artificial Bee Colony and SVM Method for Microarray Gene Selection and Multi Class Cancer Classification

2016 ◽  
Vol 6 (3) ◽  
pp. 184-190 ◽  
Author(s):  
Hala M. Alshamlan ◽  
◽  
Ghada H. Badr ◽  
Yousef A. Alohali
Keyword(s):  
Artificial Bee Colony ◽  
Gene Selection ◽  
Cancer Classification ◽  
Bee Colony ◽  
Microarray Gene

Selection of Pathway Markers for Cancer Using Collaborative Binary Multi-Swarm Optimization

2018 ◽  
pp. 337-363
Author(s):  
Prativa Agarwalla ◽  
Sumitra Mukhopadhyay
Keyword(s):  
Particle Swarm Optimization ◽  
Artificial Bee Colony ◽  
Gene Selection ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
High Accuracy ◽  
Marker Genes ◽  
Binary Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Bee Colony

Pathway information for cancer detection helps to find co-regulated gene groups whose collective expression is strongly associated with cancer development. In this paper, a collaborative multi-swarm binary particle swarm optimization (MS-BPSO) based gene selection technique is proposed that outperforms to identify the pathway marker genes. We have compared our proposed method with various statistical and pathway based gene selection techniques for different popular cancer datasets as well as a detailed comparative study is illustrated using different meta-heuristic algorithms like binary coded particle swarm optimization (BPSO), binary coded differential evolution (BDE), binary coded artificial bee colony (BABC) and genetic algorithm (GA). Experimental results show that the proposed MS-BPSO based method performs significantly better and the improved multi swarm concept generates a good subset of pathway markers which provides more effective insight to the gene-disease association with high accuracy and reliability.

scholarly journals mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Hala Alshamlan ◽  
Ghada Badr ◽  
Yousef Alohali
Keyword(s):  
Gene Expression ◽  
Gene Selection ◽  
Cancer Classification ◽  
Support Vector ◽  
Optimization Approach ◽  
Selection Algorithm ◽  
Classification Problems ◽  
Microarray Gene Expression ◽  
Abc Algorithm ◽  
Microarray Gene

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.

scholarly journals Application of Nature Inspired Soft Computing Techniques for Gene Selection: A Novel Frame Work for Classification of Cancer.

2022 ◽  
Author(s):  
Rabia Musheer Aziz
Keyword(s):  
Gene Selection ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Cancer Classification ◽  
Prediction Errors ◽  
Bee Colony ◽  
Proposed Model ◽  
Comparison Results ◽  
Soft Computing Techniques

Abstract A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is applied for cancer classification, that reduces the classifier's prediction errors and allows for faster convergence by selecting informative genes. Cuckoo search (CS) algorithm was used in the onlooker bee phase (exploitation phase)of ABC to boost performance by maintaining the balance between exploration and exploitation of ABC. Tuned the modified ABC algorithm by using Naïve Bayes (NB) classifiers to improve the further accuracy of the model. Independent Component Analysis (ICA) is used for dimensionality reduction. In the first step, the reduced dataset is optimized by using Modified ABC and after that, in the second step, the optimized dataset is used to train the NB classifier. Extensive experiments were performed for comprehensive comparative analysis of the proposed algorithm with well-known metaheuristic algorithms, namely Genetic Algorithm (GA) when used with the same framework for the classification of six high-dimensional cancer datasets. The comparison results showed that the proposed model with the CS algorithm achieves the highest performance as maximum classification accuracy with less count of selected genes. This shows the effectiveness of the proposed algorithm which is validated using ANOVA for cancer classification.

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