Junliang Shang

An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions.

SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.

A joint-L2,1-norm-constraint-based semi-supervised feature extraction for RNA-Seq data analysis

It is of urgency to effectively identify differentially expressed genes from RNA-Seq data. In this paper, we proposed a novel method, joint-L2,1-norm-constraint-based semi-supervised feature extraction (L21SFE), to analyze RNA-Seq data. Our scheme was shown as follows. Firstly, we constructed a graph Laplacian matrix and refined it by using the labeled samples. Our graph construction method can make full use of a large number of unlabelled samples. Secondly, we found semi-supervised optimal maps by solving a generalized eigenvalue problem. Thirdly, we solved an optimal problem via the joint L2,1-norm constraint to obtain a projection matrix. It can diminish the impact of noises and outliers by using the L2,1-norm constraint and produce more precise results. Finally, we identified differentially expressed genes based on the projection matrix. The results on simulation and real RNA-Seq data sets demonstrated the feasibility and effectiveness of our method.

CINOEDV: a co-information based method for detecting and visualizing n-order epistatic interactions.

BackgroundDetecting and visualizing nonlinear interaction effects of single nucleotide polymorphisms (SNPs) or epistatic interactions are important topics in bioinformatics since they play an important role in unraveling the mystery of “missing heritability”. However, related studies are almost limited to pairwise epistatic interactions due to their methodological and computational challenges.ResultsWe develop CINOEDV (Co-Information based N-Order Epistasis Detector and Visualizer) for the detection and visualization of epistatic interactions of their orders from 1 to n (n ≥ 2). CINOEDV is composed of two stages, namely, detecting stage and visualizing stage. In detecting stage, co-information based measures are employed to quantify association effects of n-order SNP combinations to the phenotype, and two types of search strategies are introduced to identify n-order epistatic interactions: an exhaustive search and a particle swarm optimization based search. In visualizing stage, all detected n-order epistatic interactions are used to construct a hypergraph, where a real vertex represents the main effect of a SNP and a virtual vertex denotes the interaction effect of an n-order epistatic interaction. By deeply analyzing the constructed hypergraph, some hidden clues for better understanding the underlying genetic architecture of complex diseases could be revealed.ConclusionsExperiments of CINOEDV and its comparison with existing state-of-the-art methods are performed on both simulation data sets and a real data set of age-related macular degeneration. Results demonstrate that CINOEDV is promising in detecting and visualizing n-order epistatic interactions. CINOEDV is implemented in R and is freely available from R CRAN: http://cran.r-project.org and https://sourceforge.net/projects/cinoedv/files/.

Detection of SNP-SNP Interaction based on the Generalized Particle Swarm Optimization Algorithm

Most of complex diseases are believed to be mainly caused by epistatic interactions of pair single nucleotide poly-morphisms (SNPs), namely, SNP-SNP interactions. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing due to their mathematical and computational complexities. In this study, we proposed a method, PSOMiner, based on the generalized particle swarm optimization algorithm, with mutual information as its fitness function, for the detection of SNP-SNP interaction that has the highest pathogenic effect in a SNP data set. Experiments of PSOMiner are performed on six simulation data sets under the criteria of detection power. Results demonstrate that PSOMiner is promising for the detection of SNP-SNP interaction. In addition, the application of PSOMiner on a real age-related macular degeneration (AMD) data set provides several new clues for the exploration of AMD associated SNPs that have not been described previously. PSOMiner might be an alternative to existing methods for detecting SNP-SNP interactions.

epiACO - a method for identifying epistasis based on ant Colony optimization algorithm

BackgroundIdentifying epistasis or epistatic interactions, which refer to nonlinear interaction effects of single nucleotide polymorphisms (SNPs), is essential to understand disease susceptibility and to detect genetic architectures underlying complex diseases. Though many works have been done for identifying epistatic interactions, due to their methodological and computational challenges, the algorithmic development is still ongoing.ResultsIn this study, a method epiACO is proposed to identify epistatic interactions, which based on ant colony optimization algorithm. Highlights of epiACO are the introduced fitness function Svalue, path selection strategies, and a memory based strategy. The Svalue leverages the advantages of both mutual information and Bayesian network to effectively and efficiently measure associations between SNP combinations and the phenotype. Two path selection strategies, i.e., probabilistic path selection strategy and stochastic path selection strategy, are provided to adaptively guide ant behaviors of exploration and exploitation. The memory based strategy is designed to retain candidate solutions found in the previous iterations, and compare them to solutions of the current iteration to generate new candidate solutions, yielding a more accurate way for identifying epistasis.ConclusionsExperiments of epiACO and its comparison with other recent methods epiMODE, TEAM, BOOST, SNPRuler, AntEpiSeeker, AntMiner, MACOED, and IACO are performed on both simulation data sets and a real data set of age-related macular degeneration. Results show that epiACO is promising in identifying epistasis and might be an alternative to existing methods.

SIPSO: Selectively Informed Particle Swarm Optimization Based on Mutual Information to Determine SNP-SNP Interactions

Interactive effects of Single Nucleotide Polymorphisms (SNPs), namely, SNP-SNP interactions, have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for their detection, the algorithmic development is still ongoing due to their computational complexities. In this study, we apply selectively informed particle swarm optimization (SIPSO) to determine SNP-SNP interactions with mutual information as its fitness function. The highlights of SIPSO are the introductions of scale-free networks as its population structure, and different learning strategies as its interaction modes, considering the heterogeneity of particles. Experiments are performed on both simulation and real data sets, which show that SIPSO is promising in inferring SNP-SNP interactions, and might be an alternative to existing methods. The software package is available online at http://www.bdmb-web.cn/index.php?m=content&c=index&a=show&catid=37&id=99.

Hypergraph Supervised Search for Inferring Multiple Epistatic Interactions with Different Orders

Nonlinear interactive effects of Single Nucleotide Polymorphisms (SNPs), namely, epistatic interactions, have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for their detection, most only focus on the detection of pairwise epistatic interactions. In this study, a Hypergraph Supervised Search (HgSS) is developed based on the co-information measure for inferring multiple epistatic interactions with different orders at a substantially reduced time cost. The co-information measure is employed to exhaustively quantify the interaction effects of low order SNP combinations, as well as the main effects of SNPs. Then, highly suspected SNP combinations and SNPs are used to construct a hypergraph. By deeply analyzing the hypergraph, some clues for better understanding the genetic architecture of complex diseases could be revealed. Experiments are performed on both simulation and real data sets. Results show that HgSS is promising in inferring multiple epistatic interactions with different orders.

Adaptive non‐local means on local principle neighborhood for noise/artifacts reduction in low‐dose CT images

Purpose Low‐dose CT (LDCT) technique can reduce the x‐ray radiation exposure to patients at the cost of degraded images with severe noise and artifacts. Non‐local means (NLM) filtering has shown its potential in improving LDCT image quality. However, currently most NLM‐based approaches employ a weighted average operation directly on all neighbor pixels with a fixed filtering parameter throughout the NLM filtering process, ignoring the non‐stationary noise nature of LDCT images. In this paper, an adaptive NLM filtering scheme on local principle neighborhoods (PC‐NLM) is proposed for structure‐preserving noise/artifacts reduction in LDCT images. Methods Instead of using neighboring patches directly, in the PC‐NLM scheme, the principle component analysis (PCA) is first applied on local neighboring patches of the target patch to decompose the local patches into uncorrelated principle components (PCs), then a NLM filtering is used to regularize each PC of the target patch and finally the regularized components is transformed to get the target patch in image domain. Especially, in the NLM scheme, the filtering parameter is estimated adaptively from local noise level of the neighborhood as well as the signal‐to‐noise ratio (SNR) of the corresponding PC, which guarantees a “weaker” NLM filtering on PCs with higher SNR and a “stronger” filtering on PCs with lower SNR. The PC‐NLM procedure is iteratively performed several times for better removal of the noise and artifacts, and an adaptive iteration strategy is developed to reduce the computational load by determining whether a patch should be processed or not in next round of the PC‐NLM filtering. Results The effectiveness of the presented PC‐NLM algorithm is validated by experimental phantom studies and clinical studies. The results show that it can achieve promising gain over some state‐of‐the‐art methods in terms of artifact suppression and structure preservation. Conclusions With the use of PCA on local neighborhoods to extract principal structural components, as well as adaptive NLM filtering on PCs of the target patch using filtering parameter estimated based on the local noise level and corresponding SNR, the proposed PC‐NLM method shows its efficacy in preserving fine anatomical structures and suppressing noise/artifacts in LDCT images.

Comparison of Non-negative Matrix Factorization Methods for Clustering Genomic Data

Non-negative matrix factorization (NMF) is a useful method of data dimensionality reduction and has been widely used in many fields, such as pattern recognition and data mining. Compared with other traditional methods, it has unique advantages. And more and more improved NMF methods have been provided in recent years and all of these methods have merits and demerits when used in different applications. Clustering based on NMF methods is a common way to reflect the properties of methods. While there are no special comparisons of clustering experiments based on NMF methods on genomic data. In this paper, we analyze the characteristics of basic NMF and its classical variant methods. Moreover, we show the clustering results based on the coefficient matrix decomposed by NMF methods on the genomic datasets. We also compare the clustering accuracies and the cost of time of these methods.

Gene Extraction Based on Sparse Singular Value Decomposition

In this paper, we develop a new feature extraction method based on sparse singular value decomposition (SSVD). We apply SSVD algorithm to select the characteristic genes from Colorectal Cancer (CRC) genomic dataset, and then the differentially expressed genes obtained are evaluated by the tools based on Gene Ontology. As a gene extraction method, SSVD is also compared with some existing feature extraction methods such as independent component analysis (ICA), the p-norm robust feature extraction (PREE) and sparse principal component analysis (SPCA). The experimental results show that SSVD method outperforms the existing algorithms.