A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge. <s> BIB001 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> Genome wide protein networks have become reality in recent years due to high throughput methods for detecting protein interactions. Recent studies show that a networked representation of proteins provides a more accurate model of biological systems and processes compared to conventional pair-wise analyses. Complementary to the availability of protein networks, various graph analysis techniques have been proposed to mine these networks for pathway discovery, function assignment, and prediction of complex membership. In this paper, we propose using random walks on graphs for the complex/pathway membership problem. We evaluate the proposed technique on three different probabilistic yeast networks using a benchmark dataset of 27 complexes from the MIPS complex catalog database and 10 pathways from the KEGG pathway database. Furthermore, we compare the proposed technique to two other existing techniques both in terms of accuracy and running time performance, thus addressing the scalability issue of such analysis techniques for the first time. Our experiments show that the random walk technique achieves similar or better accuracy with more than 1,000 times speed-up compared to the best competing technique. <s> BIB002 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> In the era of globalization, traditional theories and models of social systems are shifting their focus from isolation and independence to networks and connectedness. Analyzing these new complex social models is a growing, and computationally demanding area of research. In this study, we investigate the integration of genetic algorithms (GAs) with a random-walk-based distance measure to find subgroups in social networks. We test our approach by synthetically generating realistic social network data sets. Our clustering experiments using random-walk-based distances reveal exceptionally accurate results compared with the experiments using Euclidean distances. <s> BIB003 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> Abstract Clustering is a branch of multivariate analysis that is used to create groups of data. While there are currently a variety of techniques that are used for creating clusters, many require defining additional information, including the actual number of clusters, before they can be carried out. The case study of this research presents a novel neural network that is capable of creating groups by using a combination of hierarchical clustering and self-organizing maps, without requiring the number of existing clusters to be specified beforehand. Key words: Clustering, SOM, hierarchical clustering, PAM, Dendrogram, 1. Introduction Cluster analysis is a branch of multivariate statistical analysis that is used for detecting patterns in the classification of elements. Cluster analysis is used in a wide variety of fields including bioinformatics [9] [19] and surveillance [14] [15]. The methods used for clustering differ considerably according to the type of data and the amount of available information. Clustering techniques are typically broken down into the following categories [18] [19] <s> BIB004 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> Abstract Intelligent Kernel K-Means is a fully unsupervised clustering algorithm based on kernel. It is able to cluster kernel matrix without any information regarding to the number of required clusters. Our experiment using gene expression of human colorectal carcinoma had shown that the genes were grouped into three clusters. Global silhouette value and davies-bouldin index of the resulted clusters indicated that they are trustworthy and compact. To analyze the relationship between the clustered genes and phenotypes of clinical data, we performed correlation (CR) between each of three phenotypes (distant metastasis, cancer and normal tissues, and lymph node) with genes in each cluster of original dataset and permuted dataset. The result of the correlation had shown that Cluster 1 and Cluster 2 of original dataset had significantly higher CR than that of the permuted dataset. Among the three clusters, Cluster 3 contained smallest number of genes, but 16 out of 21 genes in that cluster were genes listed in Tumor Classifier List (TCL). <s> BIB005 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> Gene selection (GS) is an important research area in the analysis of DNA-microarray data, since it involves gene discovery meaningful for a particular target annotation or able to discriminate expression profiles of samples coming from different populations. In this context, a wide number of filter methods have been proposed in the literature to identify subsets of relevant genes in accordance with prefixed targets. Despite the fact that there is a wide number of proposals, the complexity imposed by this problem (GS) remains a challenge. Hence, this paper proposes a novel approach for gene selection by using cluster techniques and filter methods on the found groupings to achieve informative gene subsets. As a result of applying our methodology to Colon cancer data, we have identified the best informative gene subset between several one subsets. According to the above, the reached results have proven the reliability of the approach given in this paper. <s> BIB006 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <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> BIB007 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data. <s> BIB008 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> Single-cell RNA sequencing (scRNA-seq) allows researchers to collect large catalogues detailing the transcriptomes of individual cells. Unsupervised clustering is of central importance for the analysis of these data, as it is used to identify putative cell types. However, there are many challenges involved. We discuss why clustering is a challenging problem from a computational point of view and what aspects of the data make it challenging. We also consider the difficulties related to the biological interpretation and annotation of the identified clusters. <s> BIB009 </s> A Review of Computational Methods for Clustering Genes with Similar Biological Functions <s> Discussion <s> Abstract Infrared Thermography (IRT) is a method that has modernized the way for monitoring the thermal conditions, finding some potential faults or defects that could be available in electrical systems. In the proposed work, IRT electrical images are taken for diagnosing the faults by the image pre-processing and segmentation process. Initially, the IRT images are changed over into a grayscale image, trailed by image pre-processing is performed where histogram equalization is applied. With the intention of segmenting the faulty portion (high temperature zone) from the electrical equipment, Fuzzy C Means (FCM) strategy is introduced. For optimizing the centroid of FCM algorithm Modified Ant Lion Optimization (MALO) is proposed. From the segmented images, small size portions are removed by using Region Pros function. This operation can remove the isolated pixels from the image and extract image components for better representation of images. The optimum results show that the proposed work accomplishes maximum segmentation accuracy compared to existing segmentation algorithms. <s> BIB010
An efficient clustering technique is the one capable of extracting useful information about the behavior of a gene. According to Oyelade et al.  , ensemble clustering (a combination of two or more phases of clustering) can generate more robust and better quality clusters compared to single clustering. Table 8 summarizes the ensemble methods for clustering that were used by previous researchers. In addition, Oyelade et al.  also showed that hierarchical clustering is more suitable to handle real datasets, such as image data, compared to partitioning clustering, but it is computationally expensive. Advanced technological developments can isolate a large group of cells. Biological data can provide a better understanding of the complex biological processes. For example, single-cell RNA sequencing can help to expose biological processes and medical insights BIB008 . The k-means clustering typically performs better than hierarchical clustering in smaller datasets, but it requires a long computational time  BIB008 . Other than that, large amounts of bulk data can address biological dynamics and cancer heterogeneity. Tang et al. BIB008 proposed High-order Correlation Integration (HCI), which uses k-means clustering and Pearson's correlation coefficient in the experiments. Their results showed that HCI outperforms the existing methods (k-means clustering and hierarchical clustering) under single-cell and bulk RNA-seq datasets. Unsupervised clustering is one of the powerful techniques used in single-cell RNA sequencing to define cell types based on the transcriptome BIB009 . Fully unsupervised clustering techniques (e.g., intelligent k-means and kernel k-means) are applied to analyze genes in colorectal carcinoma BIB005 . Other than that, random walk-based clustering, GCHL, and CLIQUE clustering techniques are also used in unsupervised manners   BIB002 BIB003    . The purpose of optimization for objective function and validation is to achieve quality clusters. Most of the previous studies used swarm intelligence to optimize the parameters of clustering techniques and to identify the optimal number of possible clusters BIB010 . The objective function of clustering techniques defines optimization as maximizing the accuracy of the centroid or the cluster center, especially for partitioning clustering techniques. It is because partitioning clustering needs to initialize either the number of clusters or the number of centroids beforehand. Furthermore, clustering validation is also essential to measure within or between the identified clusters BIB006 . In this research, leukemia data containing 3051 genes and 38 samples BIB001 were used to evaluate the performance of each clustering techniques category. The genes obtained by the clustering techniques were different from one technique category to another; however, the number of target clusters was the same among the techniques. As a result, the grid-based clustering technique provided higher classification accuracy than other clustering techniques. The technique was able to identify 7.29% of the prognostic markers in leukemia data. On the other hand, k-means clustering achieved the highest percentage (8%) of identifying prognostic markers in leukemia, but the classification accuracy in this case was quite poor. A summary of optimal cluster analysis studied by previous researchers is shown in Table 9 . According to the table, k-means clustering was the most used in the research. Integration of optimization is critical to its use in research because it can solve the issue of k-means clustering that requires initializing the number of clusters beforehand BIB004 BIB007 . Table 9 . Summary of optimal cluster analysis.