Current Issue : July - September Volume : 2017 Issue Number : 3 Articles : 6 Articles
Background: Gene regulatory interactions are of fundamental importance to various biological functions and\nprocesses. However, only a few previous computational studies have claimed success in revealing genome-wide\nregulatory landscapes from temporal gene expression data, especially for complex eukaryotes like human. Moreover,\nrecent work suggests that these methods still suffer from the curse of dimensionality if a network size increases to 100\nor higher.\nResults: Here we present a novel scalable algorithm for identifying genome-wide gene regulatory network (GRN)\nstructures, and we have verified the algorithm performances by extensive simulation studies based on the DREAM\nchallenge benchmark data. The highlight of our method is that its superior performance does not degenerate even\nfor a network size on the order of 104, and is thus readily applicable to large-scale complex networks. Such a\nbreakthrough is achieved by considering both prior biological knowledge and multiple topological properties (i.e.,\nsparsity and hub gene structure) of complex networks in the regularized formulation. We also validate and illustrate\nthe application of our algorithm in practice using the time-course gene expression data from a study on human\nrespiratory epithelial cells in response to influenza A virus (IAV) infection, as well as the CHIP-seq data from ENCODE on\ntranscription factor (TF) and target gene interactions. An interesting finding, owing to the proposed algorithm, is that\nthe biggest hub structures (e.g., top ten) in the GRN all center at some transcription factors in the context of epithelial\ncell infection by IAV.\nConclusions: The proposed algorithm is the first scalable method for large complex network structure identification.\nThe GRN structure identified by our algorithm could reveal possible biological links and help researchers to choose\nwhich gene functions to investigate in a biological event. The algorithm described in this article is implemented in\nMATLAB, and the source code is freely available from https://github.com/Hongyu-Miao/DMI.git....
This paper presented the issues of true representation and a reliable measure for analyzing the DNA base calling is provided. The\nmethod implemented dealt with the data set quality in analyzing DNA sequencing, it is investigating solution of the problem of\nusing Neurofuzzy techniques for predicting the confidence value for each base in DNA base calling regarding collecting the data\nfor each base in DNA, and the simulation model of designing the ANFIS contains three subsystems and main system; obtain the\nthree features from the subsystems and in the main system and use the three features to predict the confidence value for each base.\nThis is achieving effective results with high performance in employment....
Gene regulation is a series of processes that control gene expression and its extent. The connections among genes and their\nregulatory molecules, usually transcription factors, and a descriptive model of such connections are known as gene regulatory\nnetworks (GRNs). Elucidating GRNs is crucial to understand the inner workings of the cell and the complexity of gene interactions.\nTo date, numerous algorithms have been developed to infer gene regulatory networks. However, as the number of identified genes\nincreases and the complexity of their interactions is uncovered, networks and their regulatory mechanisms become cumbersome\nto test. Furthermore, prodding through experimental results requires an enormous amount of computation, resulting in slow data\nprocessing. Therefore, new approaches are needed to expeditiously analyze copious amounts of experimental data resulting from\ncellular GRNs. To meet this need, cloud computing is promising as reported in the literature. Here, we propose new MapReduce\nalgorithms for inferring gene regulatory networks on a Hadoop cluster in a cloud environment. These algorithms employ an\ninformation-theoretic approach to infer GRNs using time-series microarray data. Experimental results show that our MapReduce\nprogram is much faster than an existing tool while achieving slightly better prediction accuracy than the existing tool....
Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data\nthrough computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been\ninaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect\ninteraction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion\non specific methods to solve problems related to cascade errors is still lacking. In fact, the experiments conducted by the past studies\nwere not specifically geared towards proving the ability of GRN prediction methods in avoiding the occurrences of cascade errors.\nHence, this research aims to propose Multiple Linear Regression (MLR) to infer GRN from gene expression data and to avoid\nwrongly inferring of an indirect interaction (A ââ? â?? B ââ? â?? C) as a direct interaction (A ââ? â?? C). Since the number of observations of\nthe real experiment datasets was far less than the number of predictors, some predictors were eliminated by extracting the random\nsubnetworks from global interaction networks via an established extraction method. In addition, the experiment was extended to\nassess the effectiveness of MLR in dealing with cascade error by using a novel experimental procedure that had been proposed\nin this work. The experiment revealed that the number of cascade errors had been very minimal. Apart from that, the Belsley\ncollinearity test proved that multicollinearity did affect the datasets used in this experiment greatly. All the tested subnetworks\nobtained satisfactory results, with AUROC values above 0.5....
Abstract\nBackground: With the increase in the amount of DNA methylation and gene expression data, the epigenetic\nmechanisms of cancers can be extensively investigate. Available methods integrate the DNA methylation and gene\nexpression data into a network by specifying the anti-correlation between them. However, the correlation between\nmethylation and expression is usually unknown and difficult to determine.\nResults: To address this issue, we present a novel multiple network framework for epigenetic modules, namely,\nEpigenetic Module based on Differential Networks (EMDN) algorithm, by simultaneously analyzing DNA methylation\nand gene expression data. The EMDN algorithm prevents the specification of the correlation between methylation\nand expression. The accuracy of EMDN algorithm is more efficient than that of modern approaches. On the basis of\nThe Cancer Genome Atlas (TCGA) breast cancer data, we observe that the EMDN algorithm can recognize positively\nand negatively correlated modules and these modules are significantly more enriched in the known pathways than\nthose obtained by other algorithms. These modules can serve as bio-markers to predict breast cancer subtypes by\nusing methylation profiles, where positively and negatively correlated modules are of equal importance in the\nclassification of cancer subtypes. Epigenetic modules also estimate the survival time of patients, and this factor is\ncritical for cancer therapy.\nConclusions: The proposed model and algorithm provide an effective method for the integrative analysis of DNA\nmethylation and gene expression. The algorithm is freely available as an R-package at https://github.com/\nwilliam0701/EMDN....
Background: Gene expression time series data are usually in the form of high-dimensional arrays. Unfortunately, the\ndata may sometimes contain missing values: for either the expression values of some genes at some time points or\nthe entire expression values of a single time point or some sets of consecutive time points. This significantly affects\nthe performance of many algorithms for gene expression analysis that take as an input, the complete matrix of gene\nexpression measurement. For instance, previous works have shown that gene regulatory interactions can be\nestimated from the complete matrix of gene expression measurement. Yet, till date, few algorithms have been\nproposed for the inference of gene regulatory network from gene expression data with missing values.\nResults: We describe a nonlinear dynamic stochastic model for the evolution of gene expression. The model\ncaptures the structural, dynamical, and the nonlinear natures of the underlying biomolecular systems. We present\npoint-based Gaussian approximation (PBGA) filters for joint state and parameter estimation of the system with\none-step or two-step missing measurements. The PBGA filters use Gaussian approximation and various quadrature rules,\nsuch as the unscented transform (UT), the third-degree cubature rule and the central difference rule for computing\nthe related posteriors. The proposed algorithm is evaluated with satisfying results for synthetic networks, in silico\nnetworks released as a part of the DREAM project, and the real biological network, the in vivo reverse engineering and\nmodeling assessment (IRMA) network of yeast Saccharomyces cerevisiae.\nConclusion: PBGA filters are proposed to elucidate the underlying gene regulatory network (GRN) from time series\ngene expression data that contain missing values. In our state-space model, we proposed a measurement model that\nincorporates the effect of the missing data points into the sequential algorithm. This approach produces a better\ninference of the model parameters and hence, more accurate prediction of the underlying GRN compared to when\nusing the conventional Gaussian approximation (GA) filters ignoring the missing data points....
Loading....