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https://www.readbyqxmd.com/read/28227700/prediction-of-oral-cancer-recurrence-using-dynamic-bayesian-networks
#1
Konstantina Kourou, George Rigas, Konstantinos P Exarchos, Costas Papaloukas, Dimitrios I Fotiadis, Konstantina Kourou, George Rigas, Konstantinos P Exarchos, Costas Papaloukas, Dimitrios I Fotiadis, George Rigas, Konstantinos P Exarchos, Dimitrios I Fotiadis, Costas Papaloukas, Konstantina Kourou
We propose a methodology for predicting oral cancer recurrence using Dynamic Bayesian Networks. The methodology takes into consideration time series gene expression data collected at the follow-up study of patients that had or had not suffered a disease relapse. Based on that knowledge, our aim is to infer the corresponding dynamic Bayesian networks and subsequently conjecture about the causal relationships among genes within the same time-slice and between consecutive time-slices. Moreover, the proposed methodology aims to (i) assess the prognosis of patients regarding oral cancer recurrence and at the same time, (ii) provide important information about the underlying biological processes of the disease...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28227292/a-descriptive-model-of-resting-state-networks-using-markov-chains
#2
H Xie, R Pal, S Mitra, H Xie, R Pal, S Mitra, H Xie, S Mitra
Resting-state functional connectivity (RSFC) studies considering pairwise linear correlations have attracted great interests while the underlying functional network structure still remains poorly understood. To further our understanding of RSFC, this paper presents an analysis of the resting-state networks (RSNs) based on the steady-state distributions and provides a novel angle to investigate the RSFC of multiple functional nodes. This paper evaluates the consistency of two networks based on the Hellinger distance between the steady-state distributions of the inferred Markov chain models...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226783/learning-a-probabilistic-boolean-network-model-from-biological-pathways-and-time-series-expression-data
#3
Vardaan Pahuja, Ritwik Kumar Layek, Pabitra Mitra, Vardaan Pahuja, Ritwik Kumar Layek, Pabitra Mitra, Vardaan Pahuja, Ritwik Kumar Layek, Pabitra Mitra
The problem of inferring a stochastic model for gene regulatory networks is addressed here. The prior biological data includes biological pathways and time-series expression data. We propose a novel algorithm to use both of these data to construct a Probabilistic Boolean Network (PBN) which models the observed dynamics of genes with a high degree of precision. Our algorithm constructs a pathway tree and uses the time-series expression data to select an optimal level of tree, whose nodes are used to infer the PBN...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28224760/a-tree-parenchyma-coupled-model-for-lung-ventilation-simulation
#4
N Pozin, S Montesantos, I Katz, M Pichelin, I Vignon-Clementel, C Grandmont
In this article we develop a lung-ventilation model. The parenchyma is described as an elastic homogenized media. It is irrigated by a space-filling dyadic resistive pipe network, which represents the tracheo-bronchial tree. In this model the tree and the parenchyma are strongly coupled. The tree induces an extra viscous term in the system constitutive relation, which leads, in the finite element framework, to a full matrix. We consider an efficient algorithm that takes advantage of the tree structure to enable a fast matrix-vector product computation...
February 22, 2017: International Journal for Numerical Methods in Biomedical Engineering
https://www.readbyqxmd.com/read/28224730/the-concept-of-crosstalk-directed-embryological-target-mining-and-its-application-to-essential-hypertension-treatment-failures
#5
REVIEW
Alan Alper Sag, Oguzhan Sal, Yagmur Kilic, Emine Meltem Onal, Mehmet Kanbay
This review aims to introduce the novel concept of embryological target mining applied to interorgan crosstalk network genesis, and applies embryological target mining to multidrug-resistant essential hypertension (a prototype, complex, undertreated, multiorgan systemic syndrome) to uncover new treatment targets and critique why existing strategies fail. Briefly, interorgan crosstalk pathways represent the next frontier for target mining in molecular medicine. This is because stereotyped stepwise organogenesis presents a unique opportunity to infer interorgan crosstalk pathways that may be crucial to discovering novel treatment targets...
February 21, 2017: Journal of Clinical Hypertension
https://www.readbyqxmd.com/read/28223187/deepnat-deep-convolutional-neural-network-for-segmenting-neuroanatomy
#6
Christian Wachinger, Martin Reuter, Tassilo Klein
We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground...
February 18, 2017: NeuroImage
https://www.readbyqxmd.com/read/28222757/spatiotemporal-dynamics-of-hiv-1-transmission-in-france-1999-2014-and-impact-of-targeted-prevention-strategies
#7
Antoine Chaillon, Asma Essat, Pierre Frange, Davey M Smith, Constance Delaugerre, Francis Barin, Jade Ghosn, Gilles Pialoux, Olivier Robineau, Christine Rouzioux, Cécile Goujard, Laurence Meyer, Marie-Laure Chaix
BACKGROUND: Characterizing HIV-1 transmission networks can be important in understanding the evolutionary patterns and geospatial spread of the epidemic. We reconstructed the broad molecular epidemiology of HIV from individuals with primary HIV-1 infection (PHI) enrolled in France in the ANRS PRIMO C06 cohort over 15 years. RESULTS: Sociodemographic, geographic, clinical, biological and pol sequence data from 1356 patients were collected between 1999 and 2014. Network analysis was performed to infer genetic relationships, i...
February 21, 2017: Retrovirology
https://www.readbyqxmd.com/read/28222114/social-networks-and-inference-about-unknown-events-a-case-of-the-match-between-google-s-alphago-and-sedol-lee
#8
Jonghoon Bae, Young-Jae Cha, Hyungsuk Lee, Boyun Lee, Sojung Baek, Semin Choi, Dayk Jang
This study examines whether the way that a person makes inferences about unknown events is associated with his or her social relations, more precisely, those characterized by ego network density that reflects the structure of a person's immediate social relation. From the analysis of individual predictions over the Go match between AlphaGo and Sedol Lee in March 2016 in Seoul, Korea, this study shows that the low-density group scored higher than the high-density group in the accuracy of the prediction over a future state of a social event, i...
2017: PloS One
https://www.readbyqxmd.com/read/28217746/integrated-regulatory-and-metabolic-networks-of-the-marine-diatom-phaeodactylum-tricornutum-predict-the-response-to-rising-co2-levels
#9
Jennifer Levering, Christopher L Dupont, Andrew E Allen, Bernhard O Palsson, Karsten Zengler
Diatoms are eukaryotic microalgae that are responsible for up to 40% of the ocean's primary productivity. How diatoms respond to environmental perturbations such as elevated carbon concentrations in the atmosphere is currently poorly understood. We developed a transcriptional regulatory network based on various transcriptome sequencing expression libraries for different environmental responses to gain insight into the marine diatom's metabolic and regulatory interactions and provide a comprehensive framework of responses to increasing atmospheric carbon levels...
January 2017: MSystems
https://www.readbyqxmd.com/read/28217085/resting-state-fmri-in-mice-reveals-anesthesia-specific-signatures-of-brain-functional-networks-and-their-interactions
#10
Qasim Bukhari, Aileen Schroeter, David M Cole, Markus Rudin
fMRI studies in mice typically require the use of anesthetics. Yet, it is known that anesthesia alters responses to stimuli or functional networks at rest. In this work, we have used Dual Regression analysis Network Modeling to investigate the effects of two commonly used anesthetics, isoflurane and medetomidine, on rs-fMRI derived functional networks, and in particular to what extent anesthesia affected the interaction within and between these networks. Experimental data have been used from a previous study (Grandjean et al...
2017: Frontiers in Neural Circuits
https://www.readbyqxmd.com/read/28215525/explicit-modeling-of-sirna-dependent-on-and-off-target-repression-improves-the-interpretation-of-screening-results
#11
Andrea Riba, Mario Emmenlauer, Amy Chen, Frederic Sigoillot, Feng Cong, Christoph Dehio, Jeremy Jenkins, Mihaela Zavolan
RNAi is broadly used to map gene regulatory networks, but the identification of genes that are responsible for the observed phenotypes is challenging, as small interfering RNAs (siRNAs) simultaneously downregulate the intended on targets and many partially complementary off targets. Additionally, the scarcity of publicly available control datasets hinders the development and comparative evaluation of computational methods for analyzing the data. Here, we introduce PheLiM (https://github.com/andreariba/PheLiM), a method that uses predictions of siRNA on- and off-target downregulation to infer gene-specific contributions to phenotypes...
February 22, 2017: Cell Systems
https://www.readbyqxmd.com/read/28211249/improving-the-interpretability-of-climate-landscape-metrics-an-ecological-risk-analysis-of-japan-s-marine-protected-areas
#12
Jorge García Molinos, Shintaro Takao, Naoki H Kumagai, Elvira S Poloczanska, Michael T Burrows, Masahiko Fujii, Hiroya Yamano
Conservation efforts strive to protect significant swaths of terrestrial, freshwater and marine ecosystems from a range of threats. As climate change becomes an increasing concern, these efforts must take into account how resilient protected spaces will be in the face of future drivers of change such as warming temperatures. Climate landscape metrics, which signal the spatial magnitude and direction of climate change, support a convenient initial assessment of potential threats to and opportunities within ecosystems to inform conservation and policy efforts where biological data are not available...
February 17, 2017: Global Change Biology
https://www.readbyqxmd.com/read/28208453/nonparametric-bayesian-inference-of-the-microcanonical-stochastic-block-model
#13
Tiago P Peixoto
A principled approach to characterize the hidden structure of networks is to formulate generative models and then infer their parameters from data. When the desired structure is composed of modules or "communities," a suitable choice for this task is the stochastic block model (SBM), where nodes are divided into groups, and the placement of edges is conditioned on the group memberships. Here, we present a nonparametric Bayesian method to infer the modular structure of empirical networks, including the number of modules and their hierarchical organization...
January 2017: Physical Review. E
https://www.readbyqxmd.com/read/28208380/inferring-hidden-states-in-langevin-dynamics-on-large-networks-average-case-performance
#14
B Bravi, M Opper, P Sollich
We present average performance results for dynamical inference problems in large networks, where a set of nodes is hidden while the time trajectories of the others are observed. Examples of this scenario can occur in signal transduction and gene regulation networks. We focus on the linear stochastic dynamics of continuous variables interacting via random Gaussian couplings of generic symmetry. We analyze the inference error, given by the variance of the posterior distribution over hidden paths, in the thermodynamic limit and as a function of the system parameters and the ratio α between the number of hidden and observed nodes...
January 2017: Physical Review. E
https://www.readbyqxmd.com/read/28205340/predictive-modeling-of-eeg-time-series-for-evaluating-surgery-targets-in-epilepsy-patients
#15
Andreas Steimer, Michael Müller, Kaspar Schindler
During the last 20 years, predictive modeling in epilepsy research has largely been concerned with the prediction of seizure events, whereas the inference of effective brain targets for resective surgery has received surprisingly little attention. In this exploratory pilot study, we describe a distributional clustering framework for the modeling of multivariate time series and use it to predict the effects of brain surgery in epilepsy patients. By analyzing the intracranial EEG, we demonstrate how patients who became seizure free after surgery are clearly distinguished from those who did not...
February 16, 2017: Human Brain Mapping
https://www.readbyqxmd.com/read/28204986/validation-workflow-for-a-clinical-bayesian-network-model-in-multidisciplinary-decision-making-in-head-and-neck-oncology-treatment
#16
Mario A Cypko, Matthaeus Stoehr, Marcin Kozniewski, Marek J Druzdzel, Andreas Dietz, Leonard Berliner, Heinz U Lemke
PURPOSE: Oncological treatment is being increasingly complex, and therefore, decision making in multidisciplinary teams is becoming the key activity in the clinical pathways. The increased complexity is related to the number and variability of possible treatment decisions that may be relevant to a patient. In this paper, we describe validation of a multidisciplinary cancer treatment decision in the clinical domain of head and neck oncology. METHOD: Probabilistic graphical models and corresponding inference algorithms, in the form of Bayesian networks, can support complex decision-making processes by providing a mathematically reproducible and transparent advice...
February 15, 2017: International Journal of Computer Assisted Radiology and Surgery
https://www.readbyqxmd.com/read/28200075/an-approach-to-infer-putative-disease-specific-mechanisms-using-neighboring-gene-networks
#17
Sahar Ansari, Michele Donato, Nafiseh Saberian, Sorin Draghici
No abstract text is available yet for this article.
February 15, 2017: Bioinformatics
https://www.readbyqxmd.com/read/28197408/module-anchored-network-inference-a-sequential-module-based-approach-to-novel-gene-network-construction-from-genomic-expression-data-on-human-disease-mechanism
#18
Annamalai Muthiah, Susanna R Keller, Jae K Lee
Different computational approaches have been examined and compared for inferring network relationships from time-series genomic data on human disease mechanisms under the recent Dialogue on Reverse Engineering Assessment and Methods (DREAM) challenge. Many of these approaches infer all possible relationships among all candidate genes, often resulting in extremely crowded candidate network relationships with many more False Positives than True Positives. To overcome this limitation, we introduce a novel approach, Module Anchored Network Inference (MANI), that constructs networks by analyzing sequentially small adjacent building blocks (modules)...
2017: International Journal of Genomics
https://www.readbyqxmd.com/read/28193452/the-core-and-beyond-in-the-language-ready-brain
#19
REVIEW
Peter Hagoort
In this paper a general cognitive architecture of spoken language processing is specified. This is followed by an account of how this cognitive architecture is instantiated in the human brain. Both the spatial aspects of the networks for language are discussed, as well as the temporal dynamics and the underlying neurophysiology. A distinction is proposed between networks for coding/decoding linguistic information and additional networks for getting from coded meaning to speaker meaning, i.e. for making the inferences that enable the listener to understand the intentions of the speaker...
February 11, 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/28192619/genomic-signatures-of-adaptation-to-wine-biological-aging-conditions%C3%A2-in-biofilm-forming-flor-yeasts
#20
A L Coi, F Bigey, S Mallet, S Marsit, G Zara, P Gladieux, V Galeote, M Budroni, S Dequin, J L Legras
The molecular and evolutionary processes underlying fungal domestication remain largely unknown despite the importance of fungi to bioindustry and for comparative adaptation genomics in eukaryotes. Wine fermentation and biological aging are performed by strains of S. cerevisiae with, respectively, pelagic fermentative growth on glucose, and biofilm aerobic growth utilizing ethanol. Here, we use environmental samples of wine and flor yeasts to investigate the genomic basis of yeast adaptation to contrasted anthropogenic environments...
February 13, 2017: Molecular Ecology
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