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https://www.readbyqxmd.com/read/28106722/using-network-extracted-ontologies-to-identify-novel-genes-with-roles-in-appressorium-development-in-the-rice-blast-fungus-magnaporthe-oryzae
#1
Ryan M Ames
Magnaporthe oryzae is the causal agent of rice blast disease, the most important infection of rice worldwide. Half the world's population depends on rice for its primary caloric intake and, as such, rice blast poses a serious threat to food security. The stages of M. oryzae infection are well defined, with the formation of an appressorium, a cell type that allows penetration of the plant cuticle, particularly well studied. However, many of the key pathways and genes involved in this disease stage are yet to be identified...
January 17, 2017: Microorganisms
https://www.readbyqxmd.com/read/28105958/dmirnet-inferring-direct-microrna-mrna-association-networks
#2
Minsu Lee, HyungJune Lee
BACKGROUND: MicroRNAs (miRNAs) play important regulatory roles in the wide range of biological processes by inducing target mRNA degradation or translational repression. Based on the correlation between expression profiles of a miRNA and its target mRNA, various computational methods have previously been proposed to identify miRNA-mRNA association networks by incorporating the matched miRNA and mRNA expression profiles. However, there remain three major issues to be resolved in the conventional computation approaches for inferring miRNA-mRNA association networks from expression profiles...
December 5, 2016: BMC Systems Biology
https://www.readbyqxmd.com/read/28105915/spectral-consensus-strategy-for-accurate-reconstruction-of-large-biological-networks
#3
Séverine Affeldt, Nataliya Sokolovska, Edi Prifti, Jean-Daniel Zucker
BACKGROUND: The last decades witnessed an explosion of large-scale biological datasets whose analyses require the continuous development of innovative algorithms. Many of these high-dimensional datasets are related to large biological networks with few or no experimentally proven interactions. A striking example lies in the recent gut bacterial studies that provided researchers with a plethora of information sources. Despite a deeper knowledge of microbiome composition, inferring bacterial interactions remains a critical step that encounters significant issues, due in particular to high-dimensional settings, unknown gut bacterial taxa and unavoidable noise in sparse datasets...
December 13, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28105913/mumal2-improving-sensitivity-in-shotgun-proteomics-using-cost-sensitive-artificial-neural-networks-and-a-threshold-selector-algorithm
#4
Fabio Ribeiro Cerqueira, Adilson Mendes Ricardo, Alcione de Paiva Oliveira, Armin Graber, Christian Baumgartner
BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by software. Most of these computer programs use a protein database to match peptide sequences to the observed spectra. The peptide-spectrum matches (PSMs) must also be assessed by computational tools since manual evaluation is not practicable...
December 15, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28105908/degreecox-a-network-based-regularization-method-for-survival-analysis
#5
André Veríssimo, Arlindo Limede Oliveira, Marie-France Sagot, Susana Vinga
BACKGROUND: Modeling survival oncological data has become a major challenge as the increase in the amount of molecular information nowadays available means that the number of features greatly exceeds the number of observations. One possible solution to cope with this dimensionality problem is the use of additional constraints in the cost function optimization. LASSO and other sparsity methods have thus already been successfully applied with such idea. Although this leads to more interpretable models, these methods still do not fully profit from the relations between the features, specially when these can be represented through graphs...
December 13, 2016: BMC Bioinformatics
https://www.readbyqxmd.com/read/28103880/the-use-of-genome-scale-metabolic-network-reconstruction-to-predict-fluxes-and-equilibrium-composition-of-n-fixing-versus-c-fixing-cells-in-a-diazotrophic-cyanobacterium-trichodesmium-erythraeum
#6
Joseph J Gardner, Nanette R Boyle
BACKGROUND: Computational, genome based predictions of organism phenotypes has enhanced the ability to investigate the biological phenomena that help organisms survive and respond to their environments. In this study, we have created the first genome-scale metabolic network reconstruction of the nitrogen fixing cyanobacterium T. erythraeum and used genome-scale modeling approaches to investigate carbon and nitrogen fluxes as well as growth and equilibrium population composition. RESULTS: We created a genome-scale reconstruction of T...
January 19, 2017: BMC Systems Biology
https://www.readbyqxmd.com/read/28103186/what-the-success-of-brain-imaging-implies-about-the-neural-code
#7
Olivia Guest, Bradley C Love
The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite fMRI's limitations, implies that certain neural coding schemes are more likely than others. For fMRI to succeed given its low temporal and spatial resolution, the neural code must be smooth at the voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we determine which coding schemes are plausible given both fMRI's successes and its limitations in measuring neural activity...
January 19, 2017: ELife
https://www.readbyqxmd.com/read/28099997/on-joint-estimation-of-gaussian-graphical-models-for-spatial-and-temporal-data
#8
Zhixiang Lin, Tao Wang, Can Yang, Hongyu Zhao
In this article, we first propose a Bayesian neighborhood selection method to estimate Gaussian Graphical Models (GGMs). We show the graph selection consistency of this method in the sense that the posterior probability of the true model converges to one. When there are multiple groups of data available, instead of estimating the networks independently for each group, joint estimation of the networks may utilize the shared information among groups and lead to improved estimation for each individual network...
January 18, 2017: Biometrics
https://www.readbyqxmd.com/read/28098166/inferring-centrality-from-network-snapshots
#9
Haibin Shao, Mehran Mesbahi, Dewei Li, Yugeng Xi
The topology and dynamics of a complex network shape its functionality. However, the topologies of many large-scale networks are either unavailable or incomplete. Without the explicit knowledge of network topology, we show how the data generated from the network dynamics can be utilised to infer the tempo centrality, which is proposed to quantify the influence of nodes in a consensus network. We show that the tempo centrality can be used to construct an accurate estimate of both the propagation rate of influence exerted on consensus networks and the Kirchhoff index of the underlying graph...
January 18, 2017: Scientific Reports
https://www.readbyqxmd.com/read/28096376/myosin-driven-transport-network-in-plants
#10
Elizabeth G Kurth, Valera V Peremyslov, Hannah L Turner, Kira S Makarova, Jaime Iranzo, Sergei L Mekhedov, Eugene V Koonin, Valerian V Dolja
We investigate the myosin XI-driven transport network in Arabidopsis using protein-protein interaction, subcellular localization, gene knockout, and bioinformatics analyses. The two major groups of nodes in this network are myosins XI and their membrane-anchored receptors (MyoB) that, together, drive endomembrane trafficking and cytoplasmic streaming in the plant cells. The network shows high node connectivity and is dominated by generalists, with a smaller fraction of more specialized myosins and receptors...
January 17, 2017: Proceedings of the National Academy of Sciences of the United States of America
https://www.readbyqxmd.com/read/28095421/approximate-inference-for-time-varying-interactions-and-macroscopic-dynamics-of-neural-populations
#11
Christian Donner, Klaus Obermayer, Hideaki Shimazaki
The models in statistical physics such as an Ising model offer a convenient way to characterize stationary activity of neural populations. Such stationary activity of neurons may be expected for recordings from in vitro slices or anesthetized animals. However, modeling activity of cortical circuitries of awake animals has been more challenging because both spike-rates and interactions can change according to sensory stimulation, behavior, or an internal state of the brain. Previous approaches modeling the dynamics of neural interactions suffer from computational cost; therefore, its application was limited to only a dozen neurons...
January 17, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28095201/multisensory-bayesian-inference-depends-on-synapse-maturation-during-training-theoretical-analysis-and-neural-modeling-implementation
#12
Mauro Ursino, Cristiano Cuppini, Elisa Magosso
Recent theoretical and experimental studies suggest that in multisensory conditions, the brain performs a near-optimal Bayesian estimate of external events, giving more weight to the more reliable stimuli. However, the neural mechanisms responsible for this behavior, and its progressive maturation in a multisensory environment, are still insufficiently understood. The aim of this letter is to analyze this problem with a neural network model of audiovisual integration, based on probabilistic population coding-the idea that a population of neurons can encode probability functions to perform Bayesian inference...
January 17, 2017: Neural Computation
https://www.readbyqxmd.com/read/28092574/dynamics-in-epistasis-analysis
#13
Aseel Awdeh, Hilary Phenix, Mads Kaern, Theodore Perkins
Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations...
January 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28092050/microwestern-arrays-for-systems-level-analysis-of-sh2-domain-containing-proteins
#14
Mark F Ciaccio, Richard B Jones
The Microwestern Array (MWA) method combines the scalability and miniaturization afforded by the Reverse Phase Lysate Array (RPLA) approach with the electrophoretic separation characteristic of the Western blot. This technology emulates the creation of an array of small Western blots on a single sheet of nitrocellulose allowing for the sensitive and quantitative measurement of hundreds of proteins from hundreds of cell lysates with minimal cost and maximal accuracy, precision, and reproducibility. The MWA is a versatile technology that can be easily configured for purposes such as antibody screening, cell signaling network inference, protein modification/phenotype regression analysis, and genomic/proteomic relationships...
2017: Methods in Molecular Biology
https://www.readbyqxmd.com/read/28090665/can-patel-s-%C3%AF-accurately-estimate-directionality-of-connections-in-brain-networks-from-fmri
#15
Yunzhi Wang, Olivier David, Xiaoping Hu, Gopikrishna Deshpande
PURPOSE: Investigating directional interactions between brain regions plays a critical role in fully understanding brain function. Consequently, multiple methods have been developed for noninvasively inferring directional connectivity in human brain networks using functional MRI (fMRI). Recent simulations by Smith et al showed that Patel's τ, a method based on higher-order statistics, was the best approach for inferring directional interactions from fMRI. Because simulations make restrictive assumptions about reality, we set out to verify this finding using experimental fMRI data obtained from a three-region network in a rat model with electrophysiological validation...
January 16, 2017: Magnetic Resonance in Medicine: Official Journal of the Society of Magnetic Resonance in Medicine
https://www.readbyqxmd.com/read/28089956/robust-causal-inference-using-directed-acyclic-graphs-the-r-package-dagitty
#16
Johannes Textor, Benito van der Zander, Mark S Gilthorpe, Maciej Liśkiewicz, George T H Ellison
Directed acyclic graphs (DAGs), which offer systematic representations of causal relationships, have become an established framework for the analysis of causal inference in epidemiology, often being used to determine covariate adjustment sets for minimizing confounding bias. DAGitty is a popular web application for drawing and analysing DAGs. Here we introduce the R package 'dagitty', which provides access to all of the capabilities of the DAGitty web application within the R platform for statistical computing, and also offers several new functions...
January 15, 2017: International Journal of Epidemiology
https://www.readbyqxmd.com/read/28087598/bayesian-network-inference-modeling-identifies-trib1-as-a-novel-regulator-of-cell-cycle-progression-and-survival-in-cancer-cells
#17
Rina Gendelman, Heming Xing, Olga K Mirzoeva, Preeti Sarde, Christina Curtis, Heidi Feiler, Paul McDonagh, Joe W Gray, Iya Khalil, W Michael Korn
Molecular networks governing cellular responses to targeted therapies are complex dynamic systems with non-intuitive behaviors. Here we applied a novel computational strategy to infer probabilistic causal relationships between network components based on gene expression. We constructed a model comprised of an ensemble of networks using multidimensional data from cell line models of cell cycle arrest caused by inhibition of MEK1/2. Through simulation of reverse-engineered Bayesian network modeling, we generated predictions of G1-S transition...
January 13, 2017: Cancer Research
https://www.readbyqxmd.com/read/28081159/osmoregulation-in-the-halophilic-bacterium-halomonas-elongata-a-case-study-for-integrative-systems-biology
#18
Viktoria Kindzierski, Silvia Raschke, Nicole Knabe, Frank Siedler, Beatrix Scheffer, Katharina Pflüger-Grau, Friedhelm Pfeiffer, Dieter Oesterhelt, Alberto Marin-Sanguino, Hans-Jörg Kunte
Halophilic bacteria use a variety of osmoregulatory methods, such as the accumulation of one or more compatible solutes. The wide diversity of compounds that can act as compatible solute complicates the task of understanding the different strategies that halophilic bacteria use to cope with salt. This is specially challenging when attempting to go beyond the pathway that produces a certain compatible solute towards an understanding of how the metabolic network as a whole addresses the problem. Metabolic reconstruction based on genomic data together with Flux Balance Analysis (FBA) is a promising tool to gain insight into this problem...
2017: PloS One
https://www.readbyqxmd.com/read/28081125/the-neural-representation-of-prospective-choice-during-spatial-planning-and-decisions
#19
Raphael Kaplan, John King, Raphael Koster, William D Penny, Neil Burgess, Karl J Friston
We are remarkably adept at inferring the consequences of our actions, yet the neuronal mechanisms that allow us to plan a sequence of novel choices remain unclear. We used functional magnetic resonance imaging (fMRI) to investigate how the human brain plans the shortest path to a goal in novel mazes with one (shallow maze) or two (deep maze) choice points. We observed two distinct anterior prefrontal responses to demanding choices at the second choice point: one in rostrodorsal medial prefrontal cortex (rd-mPFC)/superior frontal gyrus (SFG) that was also sensitive to (deactivated by) demanding initial choices and another in lateral frontopolar cortex (lFPC), which was only engaged by demanding choices at the second choice point...
January 2017: PLoS Biology
https://www.readbyqxmd.com/read/28079135/predicting-drug-target-interactions-by-dual-network-integrated-logistic-matrix-factorization
#20
Ming Hao, Stephen H Bryant, Yanli Wang
In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors...
January 12, 2017: Scientific Reports
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