A Comparative Study of Clustering Methods for Relevant Gene Selection in Microarray Data

Sardana, Manju; Agrawal, R. K.

doi:10.1007/978-3-642-30157-5_78

Cited by 8 publications

(4 citation statements)

References 20 publications

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“…A commonly applied feature selection method is clustering [26–28] (where the genes within the same cluster are highly correlated) which reduces the redundancy among the selected genes for classification. For example, a two-layer feature selection method based on clustering was employed to select genes with reduced redundancy [26].…”

Section: Classification Methodsmentioning

confidence: 99%

“…For example, a two-layer feature selection method based on clustering was employed to select genes with reduced redundancy [26]. Using four clustering methods, k-means, self-organizing map (SOM), hierarchical agglomerative and hierarchical divisive clustering, the dataset was partitioned into clusters such that the intra-cluster similarity is higher than the inter-cluster similarity.…”

Section: Classification Methodsmentioning

confidence: 99%

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A Classification Framework Applied to Cancer Gene Expression Profiles

Hijazi

Chan

2013

Journal of Healthcare Engineering

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Classification of cancer based on gene expression has provided insight into possible treatment strategies. Thus, developing machine learning methods that can successfully distinguish among cancer subtypes or normal versus cancer samples is important. This work discusses supervised learning techniques that have been employed to classify cancers. Furthermore, a two-step feature selection method based on an attribute estimation method (e.g., ReliefF) and a genetic algorithm was employed to find a set of genes that can best differentiate between cancer subtypes or normal versus cancer samples. The application of different classification methods (e.g., decision tree, k-nearest neighbor, support vector machine (SVM), bagging, and random forest) on 5 cancer datasets shows that no classification method universally outperforms all the others. However, k-nearest neighbor and linear SVM generally improve the classification performance over other classifiers. Finally, incorporating diverse types of genomic data (e.g., protein-protein interaction data and gene expression) increase the prediction accuracy as compared to using gene expression alone.

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Section: Classification Methodsmentioning

confidence: 99%

Section: Classification Methodsmentioning

confidence: 99%

A Classification Framework Applied to Cancer Gene Expression Profiles

Hijazi

Chan

2013

Journal of Healthcare Engineering

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“…In recent years, a new dimension to cancer research has been encompassed by the advent of microarray technology. For the classification, analysis, diagnosis, and treatment of cancer, a proficient method has been emerged by the microarray-based gene expression data [ 9 ]. Thousands of features termed as genes are found in the microarray gene expression dataset.…”

Section: Introductionmentioning

confidence: 99%

A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques

Prabhakar

Rajaguru²,

Won

2021

Journal of Healthcare Engineering

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In the field of bioinformatics, feature selection in classification of cancer is a primary area of research and utilized to select the most informative genes from thousands of genes in the microarray. Microarray data is generally noisy, is highly redundant, and has an extremely asymmetric dimensionality, as the majority of the genes present here are believed to be uninformative. The paper adopts a methodology of classification of high dimensional lung cancer microarray data utilizing feature selection and optimization techniques. The methodology is divided into two stages; firstly, the ranking of each gene is done based on the standard gene selection techniques like Information Gain, Relief–F test, Chi-square statistic, and T-statistic test. As a result, the gathering of top scored genes is assimilated, and a new feature subset is obtained. In the second stage, the new feature subset is further optimized by using swarm intelligence techniques like Grasshopper Optimization (GO), Moth Flame Optimization (MFO), Bacterial Foraging Optimization (BFO), Krill Herd Optimization (KHO), and Artificial Fish Swarm Optimization (AFSO), and finally, an optimized subset is utilized. The selected genes are used for classification, and the classifiers used here are Naïve Bayesian Classifier (NBC), Decision Trees (DT), Support Vector Machines (SVM), and K-Nearest Neighbour (KNN). The best results are shown when Relief-F test is computed with AFSO and classified with Decision Trees classifier for hundred genes, and the highest classification accuracy of 99.10% is obtained.

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“…In the past few decades, the research of clustering techniques has been extended to many fields [5] such as gene analysis [6] and community detection [7]. The idea of most clustering algorithms aims at making the similarity among points in a cluster higher than those from different clusters, namely to minimize the within-cluster distance [8].…”

Section: Introductionmentioning

confidence: 99%

An Entropy Regularization k-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering

Xiong

Wang

Huang

et al. 2019

Entropy

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Although within-cluster information is commonly used in most clustering approaches, other important information such as between-cluster information is rarely considered in some cases. Hence, in this study, we propose a new novel measure of between-cluster distance in subspace, which is to maximize the distance between the center of a cluster and the points that do not belong to this cluster. Based on this idea, we firstly design an optimization objective function integrating the between-cluster distance and entropy regularization in this paper. Then, updating rules are given by theoretical analysis. In the following, the properties of our proposed algorithm are investigated, and the performance is evaluated experimentally using two synthetic and seven real-life datasets. Finally, the experimental studies demonstrate that the results of the proposed algorithm (ERKM) outperform most existing state-of-the-art k-means-type clustering algorithms in most cases.

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A Comparative Study of Clustering Methods for Relevant Gene Selection in Microarray Data

Cited by 8 publications

References 20 publications

A Classification Framework Applied to Cancer Gene Expression Profiles

A Classification Framework Applied to Cancer Gene Expression Profiles

A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques

An Entropy Regularization k-Means Algorithm with a New Measure of between-Cluster Distance in Subspace Clustering

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