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Network-based inference

Wei Peng, Wei Lan, Zeng Yu, Jianxin Wang, Yi Pan
MicroRNAs have close relationship with human diseases. Therefore, identifying disease related MicroRNAs plays an important role in disease diagnosis, prognosis and therapy. However, designing an effective computational method which can make good use of various biological resources and correctly predict the associations between MicroRNA and disease is still a big challenge. Previous researchers have pointed out that there are complex relationships among microRNAs, diseases and environment factors. There are inter-relationships between microRNAs, diseases or environment factors based on their functional similarity or phenotype similarity or chemical structure similarity and so on...
November 29, 2016: IEEE Transactions on Nanobioscience
Samantha Cassar, Marie L Misso, William G Hopkins, Christopher S Shaw, Helena J Teede, Nigel K Stepto
STUDY QUESTION: What is the degree of intrinsic insulin resistance (IR) in women with polycystic ovary syndrome (PCOS) and the relative contribution of BMI to overall IR based on meta-analysis of gold standard insulin clamp studies? SUMMARY ANSWER: We report an inherent reduction (-27%) of insulin sensitivity (IS) in PCOS patients, which was independent of BMI. WHAT IS ALREADY KNOWN: PCOS is prevalent, complex and underpinned by IR but controversies surround the degree of intrinsic IR in PCOS, the effect of BMI and the impact of the different diagnostic criteria (NIH versus Rotterdam) in PCOS...
November 2016: Human Reproduction
Rafael Bargiela, Manuel Ferrer
Network reconstruction procedures based on meta-"omics" data are an invaluable tool for inferring total and active set of reactions mediated by different members in a microbial community. Within them, network-based methods for automatic analysis of catabolic capacities in metagenomes are currently limited. Here, we describe the complete workflow, scripts, and commands allowing the automatic reconstruction of biodegradation networks using as an input meta-sequences generated by direct DNA sequencing.
2017: Methods in Molecular Biology
Meng-Hui Zhang, Qin-Hai Shen, Zhao-Min Qin, Qiao-Ling Wang, Xi Chen
The objective of the present study is to identify significant genes and pathways associated with hepatocellular carcinoma (HCC) by systematically tracking the dysregulated modules of re-weighted protein-protein interaction (PPI) networks. Firstly, normal and HCC PPI networks were inferred and re-weighted based on Pearson correlation coefficient. Next, modules in the PPI networks were explored by a clique-merging algorithm, and disrupted modules were identified utilizing a maximum weight bipartite matching in non-increasing order...
November 2016: Oncology Letters
Alireza F Siahpirani, Sushmita Roy
No abstract text is available yet for this article.
November 29, 2016: Nucleic Acids Research
K Inoue, B D Valente, N Shoji, T Honda, K Oyama, G J M Rosa
Meat quality is one of the most important traits determining carcass price in the Japanese beef market. Optimized breeding goals and management practices for the improvement of meat quality traits requires knowledge regarding any potential functional relationships between them. In this context, the objective of this research was to infer phenotypic causal networks involving beef marbling score (BMS), beef color score (BCL), firmness of beef (FIR), texture of beef (TEX), beef fat color score (BFS), and the ratio of MUFA to SFA (MUS) from 11,855 Japanese Black cattle...
October 2016: Journal of Animal Science
Ariella Cohain, Aparna A Divaraniya, Kuixi Zhu, Joseph R Scarpa, Andrew Kasarskis, Jun Zhu, Rui Chang, Joel T Dudley, Eric E Schadt
Network reconstruction algorithms are increasingly being employed in biomedical and life sciences research to integrate large-scale, high-dimensional data informing on living systems. One particular class of probabilistic causal networks being applied to model the complexity and causal structure of biological data is Bayesian networks (BNs). BNs provide an elegant mathematical framework for not only inferring causal relationships among many different molecular and higher order phenotypes, but also for incorporating highly diverse priors that provide an efficient path for incorporating existing knowledge...
2016: Pacific Symposium on Biocomputing
Hyunghoon Cho, Bonnie Berger, Jian Peng
The topological landscape of molecular or functional interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, a pressing yet-unsolved challenge is how to combine multiple heterogeneous networks, each having different connectivity patterns, to achieve more accurate inference. Here, we describe the Mashup framework for scalable and robust network integration. In Mashup, the diffusion in each network is first analyzed to characterize the topological context of each node...
November 22, 2016: Cell Systems
Grace Tzun-Wen Shaw, Yueh-Yang Pao, Daryi Wang
BACKGROUND: The complexity and dynamics of microbial communities are major factors in the ecology of a system. With the NGS technique, metagenomics data provides a new way to explore microbial interactions. Lotka-Volterra models, which have been widely used to infer animal interactions in dynamic systems, have recently been applied to the analysis of metagenomic data. RESULTS: In this paper, we present the Lotka-Volterra model based tool, the Metagenomic Microbial Interacticon Simulator (MetaMIS), which is designed to analyze the time series data of microbial community profiles...
November 25, 2016: BMC Bioinformatics
Youngjune Park, Sangsoo Lim, Jin-Wu Nam, Sun Kim
Intratumor heterogeneity (ITH) is observed at different stages of tumor progression, metastasis and reouccurence, which can be important for clinical applications. We used RNA-sequencing data from tumor samples, and measured the level of ITH in terms of biological network states. To model complex relationships among genes, we used a protein interaction network to consider gene-gene dependency. ITH was measured by using an entropy-based distance metric between two networks, nJSD, with Jensen-Shannon Divergence (JSD)...
November 24, 2016: Scientific Reports
S R Bartlett, J O Wertheim, R A Bull, G V Matthews, F M J Lamoury, K Scheffler, M Hellard, L Maher, G J Dore, A R Lloyd, T L Applegate, J Grebely
Combining phylogenetic and network methodologies has the potential to better inform targeted interventions to prevent and treat infectious diseases. This study reconstructed a molecular transmission network for people with recent hepatitis C virus (HCV) infection and modelled the impact of targeting directly acting antiviral (DAA) treatment for HCV in the network. Participants were selected from three Australian studies of recent HCV from 2004 to 2014. HCV sequence data (Core-E2) from participants at the time of recent HCV detection were analysed to infer a network by connecting pairs of sequences whose divergence was ≤...
November 24, 2016: Journal of Viral Hepatitis
Ming Shi, Weiming Shen, Hong-Qiang Wang, Yanwen Chong
Inferring gene regulatory networks (GRNs) from microarray expression data are an important but challenging issue in systems biology. In this study, the authors propose a Bayesian information criterion (BIC)-guided sparse regression approach for GRN reconstruction. This approach can adaptively model GRNs by optimising the l1-norm regularisation of sparse regression based on a modified version of BIC. The use of the regularisation strategy ensures the inferred GRNs to be as sparse as natural, while the modified BIC allows incorporating prior knowledge on expression regulation and thus avoids the overestimation of expression regulators as usual...
December 2016: IET Systems Biology
Jessica J Wadley, Damien A Fordham, Vicki A Thomson, Euan G Ritchie, Jeremy J Austin
The distribution of antilopine wallaroo, Macropus antilopinus, is marked by a break in the species' range between Queensland and the Northern Territory, coinciding with the Carpentarian barrier. Previous work on M. antilopinus revealed limited genetic differentiation between the Northern Territory and Queensland M. antilopinus populations across this barrier. The study also identified a number of divergent lineages in the Northern Territory, but was unable to elucidate any geographic structure. Here, we re-examine these results to (1) determine phylogeographic patterns across the range of M...
November 2016: Ecology and Evolution
Luis F Iglesias-Martinez, Walter Kolch, Tapesh Santra
Reconstructing gene regulatory networks (GRNs) from gene expression data is a challenging problem. Existing GRN reconstruction algorithms can be broadly divided into model-free and model-based methods. Typically, model-free methods have high accuracy but are computation intensive whereas model-based methods are fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based method for inferring GRNs from time-course gene expression data. BGRMI uses a Bayesian framework to calculate the probability of different models of GRNs and a heuristic search strategy to scan the model space efficiently...
November 23, 2016: Scientific Reports
Robert Peharz, Robert Gens, Franz Pernkopf, Pedro Domingos
One of the central themes in Sum-Product networks (SPNs) is the interpretation of sum nodes as marginalized latent variables (LVs). This interpretation yields an increased syntactic or semantic structure, allows the application of the EM algorithm and to efficiently perform MPE inference. In literature, the LV interpretation was justified by explicitly introducing the indicator variables corresponding to the LVs' states. However, as pointed out in this paper, this approach is in conflict with the completeness condition in SPNs and does not fully specify the probabilistic model...
November 18, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
Kyrylo Bessonov, Kristel Van Steen
Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs, based on Conditional Inference Forests (CIFs) as proposed by Strobl et al. Our framework consists of using ensembles of Conditional Inference Trees (CITs) and selecting an appropriate aggregation scheme for variant selection prior to network construction. We show on synthetic microarray data that taking the original implementation of CIFs with conditional permutation scheme (CIFcond ) may lead to improved performance compared to Breiman's implementation of Random Forests (RF)...
December 2016: Genetic Epidemiology
Le Ou-Yang, Hong Yan, Xiao-Fei Zhang
Exploring how the structure of a gene regulatory network differs between two different disease states is fundamental for understanding the biological mechanisms behind disease development and progression. Recently, with rapid advances in microarray technologies, gene expression profiles of the same patients can be collected from multiple microarray platforms. However, previous differential network analysis methods were usually developed based on a single type of platform, which could not utilize the common information shared across different platforms...
November 21, 2016: Molecular BioSystems
David Sadowsky, Ruben Zamora, Derek Barclay, Jinling Yin, Paulo Fontes, Yoram Vodovotz
Background:Ex vivo machine perfusion (MP) can better preserve organs for transplantation. We have recently reported on the first application of an MP protocol in which liver allografts were fully oxygenated, under dual pressures and subnormothermic conditions, with a new hemoglobin-based oxygen carrier (HBOC) solution specifically developed for ex vivo utilization. In those studies, MP improved organ function post-operatively and reduced inflammation in porcine livers. Herein, we sought to refine our knowledge regarding the impact of MP by defining dynamic networks of inflammation in both tissue and perfusate...
2016: Frontiers in Pharmacology
Kamal Shadi, Saideh Bakhshi, David A Gutman, Helen S Mayberg, Constantine Dovrolis
Recent progress in diffusion MRI and tractography algorithms as well as the launch of the Human Connectome Project (HCP) have provided brain research with an abundance of structural connectivity data. In this work, we describe and evaluate a method that can infer the structural brain network that interconnects a given set of Regions of Interest (ROIs) from probabilistic tractography data. The proposed method, referred to as Minimum Asymmetry Network Inference Algorithm (MANIA), does not determine the connectivity between two ROIs based on an arbitrary connectivity threshold...
2016: Frontiers in Neuroinformatics
Gina M Calabrese, Larry D Mesner, Joseph P Stains, Steven M Tommasini, Mark C Horowitz, Clifford J Rosen, Charles R Farber
Bone mineral density (BMD) is a highly heritable predictor of osteoporotic fracture. Genome-wide association studies (GWAS) for BMD have identified dozens of associations; yet, the genes responsible for most associations remain elusive. Here, we used a bone co-expression network to predict causal genes at BMD GWAS loci based on the premise that genes underlying a disease are often functionally related and functionally related genes are often co-expressed. By mapping genes implicated by BMD GWAS onto a bone co-expression network, we predicted and inferred the function of causal genes for 30 of 64 GWAS loci...
November 15, 2016: Cell Systems
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