Clustering Algorithms: Their Application to Gene Expression Data
Jelili Oyelade,Itunuoluwa Isewon,Funke Oladipupo,Olufemi Aromolaran,Efosa Uwoghiren,Faridah Ameh,Moses Achas,Ezekiel Adebiyi +7 more
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TLDR
This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.Abstract:
Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and identify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure.read more
Citations
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References
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Journal ArticleDOI
Algorithm Note: PK-means: A new algorithm for gene clustering
Zhihua Du,Yiwei Wang,Zhen Ji +2 more
TL;DR: The results indicate that PK-means clustering is generally more accurate and less sensitive to the initial randomly selected cluster centroids than K-mean and Fuzzy K-Means and the algorithm outperforms these methods with fast convergence rate and low computation load.
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Chaotic ant swarm approach for data clustering
TL;DR: Experimental results show that the proposed clustering algorithm is an effective clustering technique and can be used to handle data sets with complex cluster sizes, densities and multiple dimensions.
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A Prototype-Based Modified DBSCAN for Gene Clustering
TL;DR: A novel DBSCAN method to cluster the gene expression data by using the prototypes produced from a squared error clustering method such as K-means, and it is observed that proposed algorithm outperforms the existing methods.
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A novel cluster validity index for fuzzy clustering based on bipartite modularity
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