A Hybrid Chimp Optimization Algorithm and Generalized Normal Distribution Algorithm with Opposition-Based Learning Strategy for Solving Data Clustering Problems

This paper is concerned with data clustering to separate clusters based on the connectivity principle for categorizing similar and dissimilar data into different groups. Although classical clustering algorithms such as K-means are efficient techniques, they often trap in local optima and have a slow convergence rate in solving high-dimensional problems. To address these issues, many successful meta-heuristic optimization algorithms and intelligence-based methods have been introduced to attain the optimal solution in a reasonable time. They are designed to escape from a local optimum problem by allowing flexible movements or random behaviors. In this study, we attempt to conceptualize a powerful approach using the three main components: Chimp Optimization Algorithm (ChOA), Generalized Normal Distribution Algorithm (GNDA), and Opposition-Based Learning (OBL) method. Firstly, two versions of ChOA with two different independent groups' strategies and seven chaotic maps, entitled ChOA(I) and ChOA(II), are presented to achieve the best possible result for data clustering purposes. Secondly, a novel combination of ChOA and GNDA algorithms with the OBL strategy is devised to solve the major shortcomings of the original algorithms. Lastly, the proposed ChOAGNDA method is a Selective Opposition (SO) algorithm based on ChOA and GNDA, which can be used to tackle large and complex real-world optimization problems, particularly data clustering applications. The results are evaluated against seven popular meta-heuristic optimization algorithms and eight recent state-of-the-art clustering techniques. Experimental results illustrate that the proposed work significantly outperforms other existing methods in terms of the achievement in minimizing the Sum of Intra-Cluster Distances (SICD), obtaining the lowest Error Rate (ER), accelerating the convergence speed, and finding the optimal cluster centers.Comment: 48 pages, 14 Tables, 12 Figure

Improving Medical Diagnosis Reliability Using Boosted C5.0 Decision Tree empowered by Particle Swarm Optimization

Improving accuracy of supervised classification algorithms in biomedical applications is one of active area of research. In this study, we improve the performance of Particle Swarm Optimization (PSO) combined with C4.5 decision tree (PSO+C4.5) classifier by applying Boosted C5.0 decision tree as the fitness function. To evaluate the effectiveness of our proposed method, it is implemented on 1 microarray dataset and 5 different medical data sets obtained from UCI machine learning databases. Moreover, the results of PSO + Boosted C5.0 implementation are compared to eight well-known benchmark classification methods (PSO+C4.5, support vector machine under the kernel of Radial Basis Function, Classification And Regression Tree (CART), C4.5 decision tree, C5.0 decision tree, Boosted C5.0 decision tree, Naive Bayes and Weighted K-Nearest neighbor). Repeated five-fold cross-validation method was used to justify the performance of classifiers. Experimental results show that our proposed method not only improve the performance of PSO+C4.5 but also obtains higher classification accuracy compared to the other classification methods

Meta-analysis of miRNA expression profiles for prostate cancer recurrence following radical prostatectomy

<div><p>Background</p><p>Prostate cancer (PCa) is a leading reason of death in men and the most diagnosed malignancies in the western countries at the present time. After radical prostatectomy (RP), nearly 30% of men develop clinical recurrence with high serum prostate-specific antigen levels. An important challenge in PCa research is to identify effective predictors of tumor recurrence. The molecular alterations in microRNAs are associated with PCa initiation and progression. Several miRNA microarray studies have been conducted in recurrence PCa, but the results vary among different studies.</p><p>Methods</p><p>We conducted a meta-analysis of 6 available miRNA expression datasets to identify a panel of co-deregulated miRNA genes and overlapping biological processes. The meta-analysis was performed using the ‘MetaDE’ package, based on combined P-value approaches (adaptive weight and Fisher's methods), in R version 3.3.1.</p><p>Results</p><p>Meta-analysis of six miRNA datasets revealed miR-125A, miR-199A-3P, miR-28-5P, miR-301B, miR-324-5P, miR-361-5P, miR-363*, miR-449A, miR-484, miR-498, miR-579, miR-637, miR-720, miR-874 and miR-98 are commonly upregulated miRNA genes, while miR-1, miR-133A, miR-133B, miR-137, miR-221, miR-340, miR-370, miR-449B, miR-489, miR-492, miR-496, miR-541, miR-572, miR-583, miR-606, miR-624, miR-636, miR-639, miR-661, miR-760, miR-890, and miR-939 are commonly downregulated miRNA genes in recurrent PCa samples in comparison to non-recurrent PCa samples. The network-based analysis showed that some of these miRNAs have an established prognostic significance in other cancers and can be actively involved in tumor growth. Gene ontology enrichment revealed many target genes of co-deregulated miRNAs are involved in “regulation of epithelial cell proliferation” and “tissue morphogenesis”. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that these miRNAs regulate cancer pathways. The PPI hub proteins analysis identified CTNNB1 as the most highly ranked hub protein. Besides, common pathway analysis showed that TCF3, MAX, MYC, CYP26A1, and SREBF1 significantly interact with those DE miRNA genes. The identified genes have been known as tumor suppressors and biomarkers which are closely related to several cancer types, such as colorectal cancer, breast cancer, PCa, gastric, and hepatocellular carcinomas. Additionally, it was shown that the combination of DE miRNAs can assist in the more specific detection of the PCa and prediction of biochemical recurrence (BCR).</p><p>Conclusion</p><p>We found that the identified miRNAs through meta-analysis are candidate predictive markers for recurrent PCa after radical prostatectomy.</p></div

P-value (or FDR) vs number of detected miRNAs for individual analysis as well as meta-analysis.

<p>In each individual dataset, moderated-t statistics was used to generate p-values while adaptive weight and Fisher's methods were utilized to combine these p-values for meta-analysis. This figure is generated using the “MetaDE” package in R.</p

ROC analysis of the best subset of the DE miRNAs in biochemical disease recurrence vs. the non-recurrence samples using each GEO datasets.

<p>The best subset of DE miRNAs is shown in the first column of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0179543#pone.0179543.t003" target="_blank">Table 3</a> which has been found by using soft computing technique (PSO/ logistic regression).</p

The 37 shared significantly deregulated miRNAs identified in the meta-analysis.

<p>The 37 shared significantly deregulated miRNAs identified in the meta-analysis.</p

Network interrelation of DE microRNAs identified in the meta-analysis.

<p>Orange squares show TF. The circles show the targets of DE microRNAs. Green and red lozenges show up regulated and down regulated microRNAs in various types of diseases. The network was generated using a MIROB web tool to explore DE microRNAs relationships and collective functions.</p

The details of 37 DE miRNAs that are involved in the interaction network, which has been drawn by MIROB.

<p>The details of 37 DE miRNAs that are involved in the interaction network, which has been drawn by MIROB.</p

Top enriched gene ontology (GO) biological process identified by functional analysis of the target genes and TFs of the DE microRNAs in the meta-analysis.

<p>Top enriched gene ontology (GO) biological process identified by functional analysis of the target genes and TFs of the DE microRNAs in the meta-analysis.</p

Best subset, PART’s decision rules and diagnostic potentials for the DE microRNAs identified from meta-analysis in 6 GEO datasets.

<p>Best subset, PART’s decision rules and diagnostic potentials for the DE microRNAs identified from meta-analysis in 6 GEO datasets.</p