Dynamic Clustering of Gene Expression

An, Lingling; Doerge, R. W.

doi:10.5402/2012/537217

Cited by 16 publications

(12 citation statements)

References 47 publications

(55 reference statements)

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“…But most of them are either supervised or semi-supervised classification [16], [17] techniques. These classification methodologies help in cancer diagnosis by classifying tumor samples as benign or malignant or any other sub types [2], [3], [4].…”

Section: A Related Workmentioning

confidence: 99%

Multiobjective Simulated Annealing-Based Clustering of Tissue Samples for Cancer Diagnosis

Acharya

Saha

Thadisina

2016

IEEE J. Biomed. Health Inform.

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In the field of pattern recognition, the study of the gene expression profiles of different tissue samples over different experimental conditions has become feasible with the arrival of microarray-based technology. In cancer research, classification of tissue samples is necessary for cancer diagnosis, which can be done with the help of microarray technology. In this paper, we have presented a multiobjective optimization (MOO)-based clustering technique utilizing archived multiobjective simulated annealing(AMOSA) as the underlying optimization strategy for classification of tissue samples from cancer datasets. The presented clustering technique is evaluated for three open source benchmark cancer datasets [Brain tumor dataset, Adult Malignancy, and Small Round Blood Cell Tumors (SRBCT)]. In order to evaluate the quality or goodness of produced clusters, two cluster quality measures viz, adjusted rand index and classification accuracy ( % CoA) are calculated. Comparative results of the presented clustering algorithm with ten state-of-the-art existing clustering techniques are shown for three benchmark datasets. Also, we have conducted a statistical significance test called t-test to prove the superiority of our presented MOO-based clustering technique over other clustering techniques. Moreover, significant gene markers have been identified and demonstrated visually from the clustering solutions obtained. In the field of cancer subtype prediction, this study can have important impact.

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Section: A Related Workmentioning

confidence: 99%

Multiobjective Simulated Annealing-Based Clustering of Tissue Samples for Cancer Diagnosis

Acharya

Saha

Thadisina

2016

IEEE J. Biomed. Health Inform.

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“…In most of the approaches some supervised classification techniques (An and Doerge 2012;Wang and Pan 2014) are used. These classification methodologies categorize the cancer tissue samples as benign or malignant or any other subtypes (Alizadeh et al 2000;Yeung and Bumgarner 2003;de Souto et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective semi-supervised clustering of tissue samples for cancer diagnosis

et al. 2015

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In the domain of bioinformatics, the clustering of gene expression profiles of different tissue samples over different experimental conditions has gained importance with the invention of micro-array based technology. This study also has some impact on cancer diagnosis. The proper classification of cancer tissue samples generated using the micro-array technology helps in detecting cancers in an automated way. In the current paper we have developed a semi-supervised clustering technique for proper partitioning of these gene expression data sets. Semi-supervised clustering is a combination of unsupervised and supervised classification techniques. It uses some amount of supervised information and a large collection of unsupervised data. Here a multi-objective based semi-supervised clustering technique is developed for solving the cancer tissue classification problem. Different combinations of objective functions are used. As the supervised information we assume that class labels of 10 % data are available. The proposed technique is evaluated for three open source benchmark cancer data sets (brain tumor data set, adult malignancy and small round blood cell tumors). Two classification quality measures, viz., Adjusted Rand Index and Classification Accuracy are used to measure the goodness of the obtained partitionings. Obtained results are compared with several state-of-the-art clustering techniques. Moreover, significant gene markers have been identified and demonstrated visually from the clustering solutions obtained.

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“…Clustering techniques are explicitly or implicitly based on quantitative measures of dissimilarity between the objects of interest, and in gene expression analysis, the key concept is to compare gene expression in two or more cell/tissue types where the gene expression are assessed by measuring the number of RNA transcripts in a cell/tissue sample. Several clustering algorithms have been applied successfully to analyze gene expression data [4]- [7], [9], [13], [15] and several others have been reported to have incorporated different similarity/distance measures to optimize the analyses of the gene expression data [1], [3], [8], [12], [14], [16]- [22], [24]. Applying clustering approach in analyzing gene expression data often involve calculation of distances or similarities among the objects of the expression profiles [1], [11].…”

Section: Introductionmentioning

confidence: 99%

Optimization of Clustering Algorithms for Gene Expression Data Analysis using Distance Measures

Makolo¹,

Adigun²

2016

IJCA

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Clustering is one of the fundamental processes of analyzing gene expression data, basically by comparing gene expression profiles or sample expression profiles. Comparing expression profiles requires a measure apart from the actual clustering algorithm to quantify how similar or dissimilar the objects under consideration are. Various clustering algorithms have been used to analyze gene expression data. Some of these algorithms reported the incorporation of similarity measures like Euclidean Distance, Pearson Correlation and mutual information for their performance. This work considered different reported clustering algorithms for gene expression data analyses and the importance of different similarity measures for optimizing these clustering algorithms. To this end, no clustering technique in all the works investigated has been applied directly on gene expression data. It is observed that the output (distance matrix) of similarity or dissimilarity measures plays the role of input to clustering techniques, and those that did not use any of the popular proximity measures applied one or two approaches such as Constrained Coherency (CoCo), Silhouette coefficient measurement, and normalization and discretization, to refine gene expression data for improved cluster quality by speeding up the learning phase, reduction of computational space and handling of noise effectively.

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Dynamic Clustering of Gene Expression

Cited by 16 publications

References 47 publications

Multiobjective Simulated Annealing-Based Clustering of Tissue Samples for Cancer Diagnosis

Multiobjective Simulated Annealing-Based Clustering of Tissue Samples for Cancer Diagnosis

Multi-objective semi-supervised clustering of tissue samples for cancer diagnosis

Optimization of Clustering Algorithms for Gene Expression Data Analysis using Distance Measures

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