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PIWI-interacting RNAs (piRNAs) are the largest class of small noncoding RNAs in animal cells; they cooperate with PIWI proteins to safeguard the genome. Although several studies have established that piRNAs can be biomarkers of cancer, it is still difficult to elucidate the exact function of piRNAs. In addition, researchers need to further investigate the interplay between piRNAs and piRNA groups in tumorigenesis. To identify cancer-associated piRNA-modules, we performed a weighted gene coexpression network analysis (WGCNA) on piRNA expression data from 11 types of cancer. Thereafter, genes associated with hub piRNAs in modules were predicted, and functional analysis of these genes was used to interpret the relation between piRNA and cancer. The results indicated that these piRNA modules have significant associations with cancer. A module with a high correlation coefficient (cor: −0.83, p value: 1.86E−128) was found; it was especially relevant to head and neck squamous cell carcinoma. Moreover, we found that hub piRNAs in modules may contribute to metastasis. This finding advances the understanding of piRNA function and its association with human cancer.

Differential evolution (DE) is an efficient intelligent optimization algorithm which has been widely applied to real-world problems, however poor in solution quality and convergence performance for complex multimodal optimization problems. To tackle this problem, a new improving strategy for DE algorithm is presented, in which crossover operator, mutation operator and a new local variables adjustment strategy are integrated together to make the DE more efficient and effective. An improved dynamic crossover rate is adopted to manage the three operators, so to decrease the computational cost of DE. To investigate the performance of the proposed DE algorithm, some frequently referred mutation operators, i.e., DE/rand/1, DE/Best/1, DE/current-to-best/1, DE/Best/2, DE/rand/2, are employed, respectively, in proposed method for comparing with standard DE algorithm which also uses the same mutation operators as our method. Three state-of-the-art evolutionary algorithms (SaDE, CoDE and CMAES) and seven large-scale optimization algorithms on seven high-dimensional optimization problems of CEC2008 are compared with the proposed algorithm. We employ Wilcoxon Signed-Rank Test to further test the difference significance of performance between our algorithm and other compared algorithms. Experimental results demonstrate that the proposed algorithm is more effective in solution quality but with less CPU time (e.g., when dimensionality equals 1000, its mean optimal fitness is less than $$1\hbox {e}{-}9$$ and the CPU time reduces by about 19.3% for function Schwefel 2.26), even with a very small population size, no matter which mutation operator is adopted.

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Support vector machine (SVM) is an important classification tool in the pattern recognition and machine learning community, but its training is a time-consuming process. To deal with this problem, we propose a novel method to mine the useful information about classification hidden in the training sample for improving the training algorithm, and every training point is assigned to a value that represents the classification information, respectively, where training points with the higher values are chosen as candidate support vectors for SVM training. The classification information value for a training point is computed based on the classification accuracy of an appropriate hyperplane for the training sample, where the hyperplane goes through the mapped target of the training point in feature space defined by a kernel function. Experimental results on various benchmark datasets show the effectiveness of our algorithm.

Two main reasons for the difficulties to search for susceptibility single-nucleotide polymorphisms (SNPs) underlying genetic diseases are that the findings are not easy to be confirmed and the interactions between potential susceptibility SNPs are not clear. Many available association studies usually presented results with significance levels but did not illustrate the stability of the results. In some sense, their performances might be unclear in real practice. In this paper, we develop a novel method based on mutual information theory and linkage disequilibrium by grouping case-control. Mutual information (MI) is used to test multiple SNPs in combining with disease status. Those SNPs contributing the maximum MI are selected as potential susceptibility SNPs. Linkage disequilibrium (LD) analysis is used to extend MI detected result so that both direct and indirect factors can be included in the final result. The purpose of case-control grouping is to generate a number of data groups by randomly sampling from target samples. Each group is assumed to have almost the same number of individuals (cases and controls), and overlap is allowed among the groups. We apply the method to each data group, and then make comparisons and intersections between the results obtained from each of the groups so as to give the final result. We implement the method by continuously grouping until the final result reaches a stable state and a highly significance level. The experimental results indicate that our method to detect susceptibility SNPs in simulated and real data sets has shown remarkable success.

Background Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted. Results We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs. Conclusion This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at: http://code.google.com/p/simulation-tool-bmc-ms9169818735220977/downloads/list.