A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Categories Usage References <s> Motivation: The discovery of novel biological knowledge from the ab initio analysis of post-genomic data relies upon the use of unsupervised processing methods, in particular clustering techniques. Much recent research in bioinformatics has therefore been focused on the transfer of clustering methods introduced in other scientific fields and on the development of novel algorithms specifically designed to tackle the challenges posed by post-genomic data. The partitions returned by a clustering algorithm are commonly validated using visual inspection and concordance with prior biological knowledge---whether the clusters actually correspond to the real structure in the data is somewhat less frequently considered. Suitable computational cluster validation techniques are available in the general data-mining literature, but have been given only a fraction of the same attention in bioinformatics. ::: ::: Results: This review paper aims to familiarize the reader with the battery of techniques available for the validation of clustering results, with a particular focus on their application to post-genomic data analysis. Synthetic and real biological datasets are used to demonstrate the benefits, and also some of the perils, of analytical clustervalidation. ::: ::: Availability: The software used in the experiments is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/ ::: ::: Contact: J.Handl@postgrad.manchester.ac.uk ::: ::: Supplementary information: Enlarged colour plots are provided in the Supplementary Material, which is available at http://dbkweb.ch.umist.ac.uk/handl/clustervalidation/ <s> BIB001 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Categories Usage References <s> Kmeans is one of the most-efficient hard-clustering algorithms. It has been successfully applied to a number of problems. However, the efficiency of kmeans is dependent on its initialization of cluster centres. Different swarm intelligence techniques are applied for clustering problem. In this work, we have considered Ant Lion Optimization (ALO) which is a stochastic global optimization models. In this work Kmeans has been integrated with ALO for optimal clustering. The statistical measures of different performance metrics has been calculated and compared. The proposed method performs preferably better than Kmeans and PSO-Kmeans in terms of sum of intracluster distances and F-measure. <s> BIB002 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Categories Usage References <s> Abstract K-Means is a popular cluster analysis method which aims to partition a number of data points into K clusters. It has been successfully applied to a number of problems. However, the efficiency of K-Means depends on its initialization of cluster centers. Different swarm intelligence techniques are applied to clustering problem for enhancing the performance. In this work a hybrid clustering approach based on K-means and Ant Lion Optimization has been considered for optimal cluster analysis. Ant Lion Optimization (ALO) is a stochastic global optimization model. The performance of the proposed algorithm is compared against the performance of K-Means, KMeans-PSO, KMeans-FA, DBSCAN and Revised DBSCAN clustering methods based on different performance metrics. Experimentation is performed on eight datasets, for which the statistical analysis is carried out. The obtained results indicate that the hybrid of K-Means and Ant Lion Optimization method performs preferably better than the other three algorithms in terms of sum of intracluster distances and F-measure. <s> BIB003 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Categories Usage References <s> Due to uncertainty and inherent noise present in gene expression data, clustering of the data is a challenging task. The common assumption of many clustering algorithms is that each gene belongs to a cluster. However, few genes are functionally inactive, i.e. not participate in any biological process during experimental conditions and should be segregated from clusters. Based on this observation, a clustering method is proposed in this article that clusters co-expressed genes and segregates functionally inactive genes and noise. The proposed method formed a cluster if the difference in expression levels of genes with a specified gene is less than a threshold t in each experimental condition; otherwise, the specified gene is marked as functionally inactive or noise. The proposed method is applied on 10 yeast gene expression data, and the result shows that it performs well over existing one. <s> BIB004
Average of sum of intra-cluster distances Internal Measure assessing cluster compactness or homogeneity. BIB002 BIB003 Connectivity Internal Degree of the connectedness of clusters. BIB004 BIB001 Davies and Bouldin (DB) index Internal Measure intra-and inter-cluster using spatial dissimilarity function. [108]