Spurious Edges Research Articles

DiNeR: a Differential graphical model for analysis of co-regulation Network Rewiring

BackgroundDuring transcription, numerous transcription factors (TFs) bind to targets in a highly coordinated manner to control the gene expression. Alterations in groups of TF-binding profiles (i.e. “co-binding changes”) can affect the co-regulating associations between TFs (i.e. “rewiring the co-regulator network”). This, in turn, can potentially drive downstream expression changes, phenotypic variation, and even disease. However, quantification of co-regulatory network rewiring has not been comprehensively studied.ResultsTo address this, we propose DiNeR, a computational method to directly construct a differential TF co-regulation network from paired disease-to-normal ChIP-seq data. Specifically, DiNeR uses a graphical model to capture the gained and lost edges in the co-regulation network. Then, it adopts a stability-based, sparsity-tuning criterion -- by sub-sampling the complete binding profiles to remove spurious edges -- to report only significant co-regulation alterations. Finally, DiNeR highlights hubs in the resultant differential network as key TFs associated with disease. We assembled genome-wide binding profiles of 104 TFs in the K562 and GM12878 cell lines, which loosely model the transition between normal and cancerous states in chronic myeloid leukemia (CML). In total, we identified 351 significantly altered TF co-regulation pairs. In particular, we found that the co-binding of the tumor suppressor BRCA1 and RNA polymerase II, a well-known transcriptional pair in healthy cells, was disrupted in tumors. Thus, DiNeR successfully extracted hub regulators and discovered well-known risk genes.ConclusionsOur method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators. Our method DiNeR makes it possible to quantify changes in co-regulatory networks and identify alterations to TF co-binding patterns, highlighting key disease regulators.

Identifying Gene Network Rewiring Based on Partial Correlation.

It is an important task to learn how gene regulatory networks change under different conditions. Several Gaussian graphical model-based methods have been proposed to deal with this task by inferring differential networks from gene expression data. However, most existing methods define the differential networks as the difference of precision matrices, which may include false differential edges caused by the change of conditional variances. In addition, prior information about the condition-specific networks and the differential networks can be obtained from other domains. It is useful to incorporate prior information into differential network analysis. In this study, we propose a new differential network analysis method to address the above challenges. Instead of using the precision matrices, we define the differential networks as the difference of partial correlations, which can exclude the spurious differential edges due to the variants of conditional variances. Furthermore, prior information from multiple hypothesis testing is incorporated using a weighted fused penalty. Simulation studies show that our method outperforms the competing methods. We also apply our method to identify the differential network between luminal A and basal-like subtypes of breast cancers and the differential network between acute myeloid leukemia tumors and normal samples. The hub genes in the differential networks identified by our method carry out important biological functions.

Tree‐based inference of species interaction networks from abundance data

AbstractThe behaviour of ecological systems mainly relies on the interactions between the species it involves. We consider the problem of inferring the species interaction network from abundance data.To be relevant, any network inference methodology needs to handle count data and to account for possible environmental effects. It also needs to distinguish between direct interactions and indirect associations and graphical models provide a convenient framework for this purpose.A simulation study shows that the proposed methodology compares well with state‐of‐the‐art approaches, even when the underlying graph strongly differs from a tree. The analysis of two datasets highlights the influence of covariates on the inferred network.Accounting for covariates is critical to avoid spurious edges. The proposed approach could be extended to perform network comparison or to look for missing species.

Autonomous root-cause fault diagnosis using symbolic dynamic based causality analysis

This paper proposes a general framework for autonomous root-cause fault diagnosis in a complex process. In this framework, as a prerequisite step after conducting fault detection, the potential root-cause candidates are selected using a contribution score-based method (e.g. accumulative rate contribution scores). Then a fully automated procedure is proposed to determine the root-cause(s) of the detected fault amongst potential candidates without a priori knowledge of the base-line model or intervention of an expert. To locate the root-cause variable(s), firstly symbolic dynamic-based normalized transfer entropy (SDNTE) is proposed to generate an initial causal graph of root-cause candidates. Then symbolic dynamic-based normalized direct transfer entropy (SDNDTE) is proposed and utilized for pruning the initial graph (i.e. discard indirect and spurious causal edges). To this aim, explicit definitions of immediate intermediate variables (IIV) and source intermediate variables (SIV) are given and systematic algorithms are developed to find them efficiently. At last, a topological approach is proposed to locate the root-cause variables according to the pruned causal graph. The proposed generalized SDNDTE approach overcomes the computational complexity in the calculation of normalized direct transfer entropy (NDTE) using conventional multi-dimensional kernel probability density functions (PDFs) fitting, which enables real-time root-cause fault diagnosis. To demonstrate the effectiveness and applicability, the proposed autonomous scheme is tested on a numerical example at first, and its computational complexity is analysed and compared to other existing works. Finally, it is validated on the Tennessee Eastman process (TEP) benchmark model.

MSB based Cellular Automata for Edge Detection

A vitalcrucial pre-processing phase in image processing, computer vision and machine learning applications is Edge Detection which detects boundaries of foreground and background objects in an image. Discrimination between significant edges and not so important spurious edges highly affects the accuracy of edge detection process. This paper introduces an approach for extraction of significant edges present in images based on cellular automata. Cellular automata is a finite state machine where every cell has a state. Existing edge detection methods are complex to implement so they have large processing time. These methods tend to produce non-satisfactory results for noisy images which have cluttered background. Some methods are so trivial that they miss part of true edges and some methods are so complex that they tend to give spurious edges which are not required. The advantage of using cellular computing approach is to enhance edge detection process by reducing complexity and processing time. Parallel processing makes this method fast and computationally imple. MATLAB results of proposed method performed on images from Mendeley Dataset are compared with results obtained from existing edge detection techniques by evaluation of MSE and PSNR values Results indicate promising performance of the proposed algorithm. Visually compared, the proposed method produces better results to identify edges more clearly and is intelligent enough to discard spurious edges even for cluttered and complex images.

Commensal bacterial sharing does not predict host social associations in kangaroos.

Social network analysis has been postulated as a tool to study potential pathogen transmission in wildlife but is resource-intensive to quantify. Networks based on bacterial genotypes have been proposed as a cost-effective method for estimating social or transmission network based on the assumption that individuals in close contact will share commensal bacteria. However, the use of network analysis to study wild populations requires critical evaluation of the assumptions and parameters these models are founded on. We test (a) whether networks of commensal bacterial sharing are related to hosts' social associations and hence could act as a proxy for estimating transmission networks, (b) how the parameters chosen to define host associations and delineate bacterial genotypes impact inference and (c) whether these relationships change across time. We use stochastic simulations to evaluate how uncertainty in parameter choice affects network structure. We focused on a well-studied population of eastern grey kangaroos (Macropus giganteus), from Sundown National Park, Australia. Using natural markings, each individual was identified and its associations with other kangaroos recorded through direct field observations over 2years to construct social networks. Faecal samples were collected, Escherichia coli was cultured and genotyped using BOX-PCR, and bacterial networks were constructed. Two individuals were connected in the bacterial network if they shared at least one E.coli genotype. We determined the capacity of bacterial networks to predict the observed social network structure in each year. We found little support for a relationship between social association and dyadic commensal bacterial similarity. Thresholds to determine host associations and similarity cut-off values used to define E.coli genotypes had important ramifications for inferring links between individuals. In fact, we found that inferences can show opposite patterns based on the chosen thresholds. Moreover, no similarity in overall bacterial network structure was detected between years. Although empirical disease transmission data are often unavailable in wildlife populations, both bacterial networks and social networks have limitations in representing the mode of transmission of a pathogen. Our results suggest that caution is needed when designing such studies and interpreting results.

Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification

Recently, techniques for applying convolutional neural networks to graph-structured data have emerged. Graph convolutional neural networks (GCNNs) have been used to address node and graph classification and matrix completion. Although the performance has been impressive, the current implementations have limited capability to incorporate uncertainty in the graph structure. Almost all GCNNs process a graph as though it is a ground-truth depiction of the relationship between nodes, but often the graphs employed in applications are themselves derived from noisy data or modelling assumptions. Spurious edges may be included; other edges may be missing between nodes that have very strong relationships. In this paper we adopt a Bayesian approach, viewing the observed graph as a realization from a parametric family of random graphs. We then target inference of the joint posterior of the random graph parameters and the node (or graph) labels. We present the Bayesian GCNN framework and develop an iterative learning procedure for the case of assortative mixed-membership stochastic block models. We present the results of experiments that demonstrate that the Bayesian formulation can provide better performance when there are very few labels available during the training process.

Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models

Background: Biological entities such as genes, promoters, mRNA, metabolites or proteins do not act alone, but in concert in their network context. Modules, i.e., groups of nodes with similar topological properties in these networks characterize important biological functions of the underlying biomolecular system. Edges in such molecular networks represent regulatory and physical interactions, and comparing them between conditions provides valuable information on differential molecular mechanisms. However, biological data is inherently noisy and network reduction techniques can propagate errors particularly to the level of edges. We aim to improve the analysis of networks of biological molecules by deriving modules together with edge relevance estimations that are based on global network characteristics. Methods: We propose to fit the networks to stochastic block models (SBM), a method that has not yet been investigated for the analysis of biomolecular networks. This procedure both delivers modules of the networks and enables the derivation of edge confidence scores. We apply it to correlation-based networks of breast cancer data originating from high-throughput measurements of diverse molecular layers such as transcriptomics, proteomics, and metabolomics. The networks were reduced by thresholding for correlation significance or by requirements on scale-freeness. Results and discussion: We find that the networks are best represented by the hierarchical version of the SBM, and many of the predicted blocks have a biological meaning according to functional annotation. The edge confidence scores are overall in concordance with the biological evidence given by the measurements. As they are based on global network connectivity characteristics and potential hierarchies within the biomolecular networks are taken into account, they could be used as additional, integrated features in network-based data comparisons. Their tight relationship to edge existence probabilities can be exploited to predict missing or spurious edges in order to improve the network representation of the underlying biological system.

DiffNetFDR: differential network analysis with false discovery rate control.

To identify biological network rewiring under different conditions, we develop a user-friendly R package, named DiffNetFDR, to implement two methods developed for testing the difference in different Gaussian graphical models. Compared to existing tools, our methods have the following features: (i) they are based on Gaussian graphical models which can capture the changes of conditional dependencies; (ii) they determine the tuning parameters in a data-driven manner; (iii) they take a multiple testing procedure to control the overall false discovery rate; and (iv) our approach defines the differential network based on partial correlation coefficients so that the spurious differential edges caused by the variants of conditional variances can be excluded. We also develop a Shiny application to provide easier analysis and visualization. Simulation studies are conducted to evaluate the performance of our methods. We also apply our methods to two real gene expression datasets. The effectiveness of our methods is validated by the biological significance of the identified differential networks. R package and Shiny app are available at https://github.com/Zhangxf-ccnu/DiffNetFDR. Supplementary data are available at Bioinformatics online.

SCOP: a novel scaffolding algorithm based on contig classification and optimization.

Scaffolding is an essential step during the de novo sequence assembly process to infer the direction and order relationships between the contigs and make the sequence assembly results more continuous and complete. However, scaffolding still faces the challenges of repetitive regions in genome, sequencing errors and uneven sequencing depth. Moreover, the accuracy of scaffolding greatly depends on the quality of contigs. Generally, the existing scaffolding methods construct a scaffold graph, and then optimize the graph by deleting spurious edges. Nevertheless, due to the wrong joints between contigs, some correct edges connecting contigs may be deleted. In this study, we present a novel scaffolding method SCOP, which is the first method to classify the contigs and utilize the vertices and edges to optimize the scaffold graph. Specially, SCOP employs alignment features and GC-content of paired reads to evaluate the quality of contigs (vertices), and divide the contigs into three types (True, Uncertain and Misassembled), and then optimizes the scaffold graph based on the classification of contigs together with the alignment of edges. The experiment results on the datasets of GAGE-A and GAGE-B demonstrate that SCOP performs better than 12 other competing scaffolders. SCOP is publicly available for download at https://github.com/bioinfomaticsCSU/SCOP. Supplementary data are available at Bioinformatics online.

Segmentation of cervical nuclei using SLIC and pairwise regional contrast.

A framework to detect and segment nuclei from cervical cytology images is proposed in this study. Poor contrast, spurious edges, degree of overlap, and intensity inhomogeneity make the nuclei segmentation task more complex in overlapping cell images. The proposed technique segments cervical nuclei by merging over-segmented SLIC superpixel regions using a novel region merging criteria based on pairwise regional contrast and image gradient contour evaluations. The framework was evaluated using the first overlapping cervical cytology image segmentation challenge - ISBI 2014 dataset. The result shows that the proposed framework outperforms the state-of-the-art algorithms in nucleus detection and segmentation accuracies.

Community detection in complex networks using link prediction

Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

Delineation of Carpal Bones From Hand X-Ray Images Through Prior Model, and Integration of Region-Based and Boundary-Based Segmentations

Image segmentation is critical and challenging in computer vision and medical image analysis. Despite decades of research, existing segmentation algorithms are still subject to typical segmentation problems, such as over-segmentation, under-segmentation, and non-closed and spurious edges. In this paper, taking the carpal bones from hand X-ray images as the foreground regions, we propose a segmentation approach to integrate segmentations from region-based and boundary-based methods to tackle these typical segmentation problems. First, adaptive local thresholding and adaptive Canny edge detection are explored to extract foreground regions and the edge map. Second, the integration of the edge map and foreground regions by XORing is proposed, to tackle the over-segmentation by adding a background boundary from the edge map near the carpal bone boundary so as to break the connection between the foreground and the over-segmented background, to handle under-segmentation by adding a foreground boundary from the edge map near the carpal bone boundary so as to enclose the missing foreground due to under-segmentation, and to complement non-closed edge and spurious edge from the edge map through the carpal bone regions from the local adaptive thresholding. Optionally, marker-controlled watershed segmentation or an active contour-based method is employed to refine the integrated segmentation. The segmented foreground regions are identified through exploring a prior model. The proposed approach has been validated on 30 representative X-ray hand images for non-overlapping carpal bones to yield an average Dice coefficient of 0.976 ± 0.006. The proposed method could provide a theory or tool for accurately segmenting images with discernible boundaries and non-uniform grayscale distribution both within the background and foreground.

Wavelet transform based novel edge detection algorithms for wideband spectrum sensing in CRNs

The primary task in cognitive radio is to dynamically explore the radio spectrum and reliably detect the co-existing licensed primary transmissions across a wide-band spectrum. This paper focuses on wavelet transform (WT) based wide-band sensing techniques, which identify the edges of the multiple frequency bands simultaneously. Novel edge detection algorithms are proposed based on continuous WT (CWT) and discrete WT (DWT) techniques, applied on wide-band power spectrum. In CWT based spectrum sensing, logarithmic scaling preceded by a thresholding is performed on the CWT coefficients to enhance the small modulus maxima values at the edges, resulting in better detection probability. Since the logarithmic scaling magnifies the spurious edges, the proposed algorithm increases the false alarm probability at high noise variance. To alleviate this problem, DWT based algorithms are proposed, where DWT performs simultaneous denoising and edge detection. To achieve good detection performance at poor SNR scenario, a moving average filtering strategy is adopted at different levels of DWT based algorithms and better performance is achieved even with lower scale value of DWT, thereby reducing the computation time. Comparative studies show that the proposed algorithms outperform the existing WT based edge detection algorithms in the dynamic and frequency selective channels as well.

Refine gene functional similarity network based on interaction networks

BackgroundIn recent years, biological interaction networks have become the basis of some essential study and achieved success in many applications. Some typical networks such as protein-protein interaction networks have already been investigated systematically. However, little work has been available for the construction of gene functional similarity networks so far. In this research, we will try to build a high reliable gene functional similarity network to promote its further application.ResultsHere, we propose a novel method to construct and refine the gene functional similarity network. It mainly contains three steps. First, we establish an integrated gene functional similarity networks based on different functional similarity calculation methods. Then, we construct a referenced gene-gene association network based on the protein-protein interaction networks. At last, we refine the spurious edges in the integrated gene functional similarity network with the help of the referenced gene-gene association network. Experiment results indicate that the refined gene functional similarity network (RGFSN) exhibits a scale-free, small world and modular architecture, with its degrees fit best to power law distribution. In addition, we conduct protein complex prediction experiment for human based on RGFSN and achieve an outstanding result, which implies it has high reliability and wide application significance.ConclusionsOur efforts are insightful for constructing and refining gene functional similarity networks, which can be applied to build other high quality biological networks.

Edge Probability and Pixel Relativity-Based Speckle Reducing Anisotropic Diffusion.

Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion-based speckle reducing filter is proposed in this paper. A probability density function of the edges along with pixel relativity information is used to control the diffusion flux flow. The probability density function helps in removing the spurious edges and the pixel relativity reduces the oversmoothing effects. Furthermore, the filtering is performed in superpixel domain to reduce the execution time, wherein a minimum of 15% of the total number of image pixels can be used. For performance evaluation, 31 frames of three synthetic images and 40 real ultrasound images are used. In most of the experiments, the proposed filter shows a better performance as compared to the state-of-the-art filters in terms of the speckle region's signal-to-noise ratio and mean square error. It also shows a comparative performance for figure of merit and structural similarity measure index. Furthermore, in the subjective evaluation, performed by the expert radiologists, the proposed filter's outputs are preferred for the improved contrast and sharpness of the object boundaries. Hence, the proposed filtering framework is suitable to reduce the unwanted speckle and improve the quality of the ultrasound images.

Link Prediction in Evolving Networks Based on Popularity of Nodes.

Link prediction aims to uncover the underlying relationship behind networks, which could be utilized to predict missing edges or identify the spurious edges. The key issue of link prediction is to estimate the likelihood of potential links in networks. Most classical static-structure based methods ignore the temporal aspects of networks, limited by the time-varying features, such approaches perform poorly in evolving networks. In this paper, we propose a hypothesis that the ability of each node to attract links depends not only on its structural importance, but also on its current popularity (activeness), since active nodes have much more probability to attract future links. Then a novel approach named popularity based structural perturbation method (PBSPM) and its fast algorithm are proposed to characterize the likelihood of an edge from both existing connectivity structure and current popularity of its two endpoints. Experiments on six evolving networks show that the proposed methods outperform state-of-the-art methods in accuracy and robustness. Besides, visual results and statistical analysis reveal that the proposed methods are inclined to predict future edges between active nodes, rather than edges between inactive nodes.

Modeling of Hob Cutter Using Comparable Gear Rack

Рассмотрена последовательность создания параметрической модели червячной зуборезной фрезы. Параметрическое моделирование имеет ряд особенностей, связанных с постоянным перестроением по изменяемым параметрам. Существуют предельные значения, при которых могут иметь место самопересечения, новые или паразитные грани, которые отсутствуют при проектировании конкретной модели инструмента. Параметрическое моделирование также предполагает создание удобного интерфейса для взаимодействия оператора с моделью. Построение производилось в системе NX, диалоговые окна создавались в приложении «Студия шаблона изделия». Было принято решение поделить проектирование на 3 составляющие. Первое - это полный автоматизированный расчет на основе базовых параметров колеса. Второе - возможность редактирования параметров модели на основе применяемых на конкретном производстве инструментов. Третье - сводная таблица всех получившихся параметров. Представлены в аналитическом виде формулы расчета фланка и протуберанца на фрезах на основе эквивалентного зубчатого колеса. Эквивалентное колесо рассчитывается через отношение инвалюты торцового угла к инвалюте угла исходного контура. Получается эквивалентное прямозубое колесо с новыми параметрами модуля и диаметрами, для которого и ведется классический расчет червячной фрезы. Представлен метод построения, основанный на эквивалентной рейке. Использование эквивалентных элементов для упрощения построений распространено, однако аппроксимация профиля при этом не должна быть больше допуска на построение, который составляет 30…50 % от допуска на измерение. Представлены результаты измерения с помощью трехмерного оптического сканера ATOSIII. Rev.02, который оказался неприменим для точного измерения элементов режущего инструмента ввиду недостаточной стабильности измерения. Главным недостатком измерения трехмерным оптическим сканером оказалась невозможность введения корректировок при дальнейшем изготовлении и переточки получаемой червячной фрезы из-за случайного характера распределения погрешностей измерения.

A framework for nucleus and overlapping cytoplasm segmentation in cervical cytology extended depth of field and volume images

We propose a framework to detect and segment nuclei and segment overlapping cytoplasm in cervical cytology images. This is a challenging task due to folded cervical cells with spurious edges, poor contrast of cytoplasm and presence of neutrophils and artifacts. The algorithm segments nuclei and cell clumps in extended depth of field (EDF) images and uses volume images to segment overlapping cytoplasm. The boundaries are first approximated by a defined similarity metric and are refined in two steps by reducing concavity, iterative smoothing and outliers removal. We evaluated our framework on two public datasets provided in the first and second overlapping cervical cell segmentation challenges (ISBI 2014 and 2015). The results show that our method outperforms other state-of-the-art algorithms on both datasets. The results on the ISBI 2014 dataset show that our method missed less than 5% of cells when the pairwise cell overlapping degree was not higher than 0.3 and it missed only 7% of cells on average in a dataset of 810 synthetic images with 4860 (overlapping) cells. On the same dataset, it outperforms other state-of-the-art methods in nucleus detection with precision 0.961 and recall 0.933. The results on the ISBI 2015 dataset containing real cervical EDF images show that our method misses around 20% of cells in EDF images where a segmentation is considered a miss if it has dice similarity coefficient not greater than 0.7. The 20% miss rate is around half of the miss rate of two other recent methods.

Road network inference through multiple track alignment

Road networks are a critical aspect of both path optimization and route planning. This paper proposes to generate the road network automatically from GPS traces through jointly aligning tracks for each road segment. First, intersections are clustered from turning points where the road users’ moving directions change. GPS traces are partitioned into small tracks for individual road segments by directly-connected intersections. The tracks for each road segment are aligned using a greedy method based on successor classification. A “forward-track” procedure is proposed to locate a warp path through jointly traversing all tracks in a way which keeps the points associated by the path element spatially close to each other. This involves an iterative procedure to cluster successor points on the tracks. The warp path produced during the alignment is used to average the tracks as the geometric representation of the road segment, and to analyze the velocity variation along the road segment. Experimental results show our method outperforms other existing methods in producing no spurious road edges and more accurate geometric road representation.