Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

Chen, Kun Huang; Wang, Kung Jeng; Tsai, Min-Lung; Wang, Kung Min; Adrian, Angelia Melani; Cheng, Wei‐Chung; Yang, Tzu Sen; Teng, Nai Chia; Tan, Kuo Pin; Chang, Ku Shang

doi:10.1186/1471-2105-15-49

Cited by 112 publications

(48 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They evaluated the performance of the proposed method (PSODT) and compare it with other algorithms of classification, such as SOM, DT (C4.5), neural networks, SVM, and Naive Bayes. The results have shown that PSODT provides better than the others methods [44].…”

Section: Classification Techniquesmentioning

confidence: 67%

A Review of Various Methods Used in the Analysis of Functional Gene Expression Data

Fyad¹,

Barigou²,

Bouamrane³

et al. 2017

IJITCS

View full text Add to dashboard Cite

show abstract

Section: Classification Techniquesmentioning

confidence: 67%

A Review of Various Methods Used in the Analysis of Functional Gene Expression Data

Fyad¹,

Barigou²,

Bouamrane³

et al. 2017

IJITCS

View full text Add to dashboard Cite

show abstract

“…This integrated algorithm involves a genetic algorithm and correlation-based heuristics for data preprocessing and data mining for making predictions. Chen et al (2014) suggested to achieved efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. The study also compares the performance of their proposed method with other well-known benchmark classification methods (support vector machine, selforganizing map, and backpropagation neural network).…”

Section: Literature Surveymentioning

confidence: 99%

Cancer Classification using Hybrid Fast Particle Swarm Optimization with Backpropagation Neural Network

Vimaladevi¹,

Kalaavathi²

2014

International Journal of Advanced Research in Computer and Comm

View full text Add to dashboard Cite

: Cancer any type of malignant growth or tumor, caused by abnormal and uncontrolled cell division: it may spread through the lymphatic system or blood stream to other parts of the body .Cancer classification is known to have the keys for addressing the problems based on cancer diagnosis and drug discovery. The proposed method of DNA microarray technique has made continuous processing thousands of gene expressions possible. Using gene expression data, the researchers have started the performance to explore the possibilities of cancer classification. The various methods have been introduced in recent years with promising results. But there are still a lot of problem which need to be addressed and understood. The performance of combining the genetic algorithm and Hybrid Fast PSO-BPN method is used to solve the optimization problems. Hybrid Fast PSO-BPN method is used to improve the accuracy and better convergence rate of genetic algorithm and this method is better for local search. This proposed method is used to overcome from the problem of computational difficulties occur by ill-condition of the square penalty function. The experimental result shows that this proposed method is better in accurate result with less execution time.

show abstract

“…This new invention of biological technology has not only brought a great impact toward biological domain but has given immense challenges to computer scientists in order to handle big data issues. Hence, even microarray technology offers a new platform in investigating cancer, analyzing large-scale of gene expression data generated by this device is not an easy task [3]. Microarray data analysis is a fast-growing since this device can be used to measure global expression of genes simultaneously.…”

Section: Introductionmentioning

confidence: 99%

“…Microarray data analysis is a fast-growing since this device can be used to measure global expression of genes simultaneously. However, analyzing and interpreting large scale of gene expression data remain a challenging issue due to their innate nature of "high dimensional low sample size" [3,4]. Microarray data mainly involved thousands of genes, n in a very small size sample, p. In addition, this data is often overwhelmed, over fitting and confused by the complexity of data analysis [5,6].…”

Section: Introductionmentioning

confidence: 99%

Filter-Based Gene Selection Method for Tissues Classification on Large Scale Gene Expression Data

Ahmad¹,

Yusof²,

Nooraini³

2018

IJET

View full text Add to dashboard Cite

DNA microarray technology is a current innovative tool that has offers a new perspective to look sight into cellular systems and measure a large scale of gene expressions at once. Regardless the novel invention of DNA microarray, most of its results relies on the computational intelligence power, which is used to interpret the large number of data. At present, interpreting large scale of gene expression data remain a thought-provoking issue due to their innate nature of "high dimensional low sample size". Microarray data mainly involved thousands of genes, n in a very small size sample, p. In addition, this data are often overwhelmed, over fitting and confused by the complexity of data analysis. Due to the nature of this microarray data, it is also common that a large number of genes may not be informative for classification purposes. For such a reason, many studies have used feature selection methods to select significant genes that present the maximum discriminative power between cancerous and normal tissues. In this study, we aim to investigate and compare the effectiveness of these four popular filter gene selection methods namely Signal-to-Noise ratio (SNR), Fisher Criterion (FC), Information Gain (IG) and t-Test in selecting informative genes that can distinguish cancer and normal tissues. Two common classifiers, Support Vector Machine (SVM) and Decision Tree (C4.5) are used to train the selected genes. These gene selection methods are tested on three large scales of gene expression datasets, namely breast cancer dataset, colon dataset, and lung dataset. This study has discovered that IG and SNR are more suitable to be used with SVM while IG fit for C4.5. In a colon dataset, SVM has achieved a specificity of 86% with SNR while and 80% for IG. In contract, C4.5 has obtained a specificity of 78% for IG on the identical dataset. These results indicate that SVM performed slightly better with IG pre-processed data compare to C4.5 on the same dataset.

show abstract

Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

Cited by 112 publications

References 40 publications

A Review of Various Methods Used in the Analysis of Functional Gene Expression Data

A Review of Various Methods Used in the Analysis of Functional Gene Expression Data

Cancer Classification using Hybrid Fast Particle Swarm Optimization with Backpropagation Neural Network

Filter-Based Gene Selection Method for Tissues Classification on Large Scale Gene Expression Data

Contact Info

Product

Resources

About