keyword
MENU ▼
Read by QxMD icon Read
search

Network-based inference

keyword
https://www.readbyqxmd.com/read/29149201/the-influence-of-filtering-and-downsampling-on-the-estimation-of-transfer-entropy
#1
Immo Weber, Esther Florin, Michael von Papen, Lars Timmermann
Transfer entropy (TE) provides a generalized and model-free framework to study Wiener-Granger causality between brain regions. Because of its nonparametric character, TE can infer directed information flow also from nonlinear systems. Despite its increasing number of applications in neuroscience, not much is known regarding the influence of common electrophysiological preprocessing on its estimation. We test the influence of filtering and downsampling on a recently proposed nearest neighborhood based TE estimator...
2017: PloS One
https://www.readbyqxmd.com/read/29145845/network-pharmacological-mechanisms-of-vernonia-anthelmintica-l-in-the-treatment-of-vitiligo-isorhamnetin-induction-of-melanogenesis-via-up-regulation-of-melanin-biosynthetic-genes
#2
Ji Ye Wang, Hong Chen, Yin Yin Wang, Xiao Qin Wang, Han Ying Chen, Mei Zhang, Yun Tang, Bo Zhang
BACKGROUND: Vitiligo is a long-term skin disease characterized by the loss of pigment in the skin. The current therapeutic approaches are limited. Although the anti-vitiligo mechanisms of Vernonia anthelmintica (L.) remain ambiguous, the herb has been broadly used in Uyghur hospitals to treat vitiligo. The overall objective of the present study aims to identify the potential lead compounds from Vernonia anthelmintica (L.) in the treatment of vitiligo via an oral route as well as the melanogenic mechanisms in the systematic approaches in silico of admetSAR and substructure-drug-target network-based inference (SDTNBI)...
November 16, 2017: BMC Systems Biology
https://www.readbyqxmd.com/read/29145805/anat-2-0-reconstructing-functional-protein-subnetworks
#3
Yomtov Almozlino, Nir Atias, Dana Silverbush, Roded Sharan
BACKGROUND: ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human. RESULTS: Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological data sets...
November 16, 2017: BMC Bioinformatics
https://www.readbyqxmd.com/read/29145449/estimation-of-the-proteomic-cancer-co-expression-sub-networks-by-using-association-estimators
#4
Cihat Erdoğan, Zeyneb Kurt, Banu Diri
In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study...
2017: PloS One
https://www.readbyqxmd.com/read/29142227/population-genetic-structure-of-the-land-snail-camaena-cicatricosa-stylommatophora-camaenidae-in-china-inferred-from-mitochondrial-genes-and-its2-sequences
#5
Weichuan Zhou, Haifang Yang, Hongli Ding, Shanping Yang, Junhong Lin, Pei Wang
The phylogeographic structure of the land snail Camaena cicatricosa was analyzed in this study based on mitochondrial gene (COI and 16srRNA, mt DNA) and internal transcribed spacer (ITS2) sequences in 347 individuals. This snail is the vector of the zoonotic food-borne parasite Angiostrongylus cantonensis and one of the main harmful snails distributed exclusively in China. The results revealed significant fixation indices of genetic differentiation and high gene flow between most populations except several populations...
November 15, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29140991/a-bayesian-method-for-detecting-pairwise-associations-in-compositional-data
#6
Emma Schwager, Himel Mallick, Steffen Ventz, Curtis Huttenhower
Compositional data consist of vectors of proportions normalized to a constant sum from a basis of unobserved counts. The sum constraint makes inference on correlations between unconstrained features challenging due to the information loss from normalization. However, such correlations are of long-standing interest in fields including ecology. We propose a novel Bayesian framework (BAnOCC: Bayesian Analysis of Compositional Covariance) to estimate a sparse precision matrix through a LASSO prior. The resulting posterior, generated by MCMC sampling, allows uncertainty quantification of any function of the precision matrix, including the correlation matrix...
November 15, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29139050/modeling-mesoscopic-cortical-dynamics-using-a-mean-field-model-of-conductance-based-networks-of-adaptive-exponential-integrate-and-fire-neurons
#7
Yann Zerlaut, Sandrine Chemla, Frederic Chavane, Alain Destexhe
Voltage-sensitive dye imaging (VSDi) has revealed fundamental properties of neocortical processing at macroscopic scales. Since for each pixel VSDi signals report the average membrane potential over hundreds of neurons, it seems natural to use a mean-field formalism to model such signals. Here, we present a mean-field model of networks of Adaptive Exponential (AdEx) integrate-and-fire neurons, with conductance-based synaptic interactions. We study a network of regular-spiking (RS) excitatory neurons and fast-spiking (FS) inhibitory neurons...
November 15, 2017: Journal of Computational Neuroscience
https://www.readbyqxmd.com/read/29133956/network-inference-from-glycoproteomics-data-reveals-new-reactions-in-the-igg-glycosylation-pathway
#8
Elisa Benedetti, Maja Pučić-Baković, Toma Keser, Annika Wahl, Antti Hassinen, Jeong-Yeh Yang, Lin Liu, Irena Trbojević-Akmačić, Genadij Razdorov, Jerko Štambuk, Lucija Klarić, Ivo Ugrina, Maurice H J Selman, Manfred Wuhrer, Igor Rudan, Ozren Polasek, Caroline Hayward, Harald Grallert, Konstantin Strauch, Annette Peters, Thomas Meitinger, Christian Gieger, Marija Vilaj, Geert-Jan Boons, Kelley W Moremen, Tatiana Ovchinnikova, Nicolai Bovin, Sakari Kellokumpu, Fabian J Theis, Gordan Lauc, Jan Krumsiek
Immunoglobulin G (IgG) is a major effector molecule of the human immune response, and aberrations in IgG glycosylation are linked to various diseases. However, the molecular mechanisms underlying protein glycosylation are still poorly understood. We present a data-driven approach to infer reactions in the IgG glycosylation pathway using large-scale mass-spectrometry measurements. Gaussian graphical models are used to construct association networks from four cohorts. We find that glycan pairs with high partial correlations represent enzymatic reactions in the known glycosylation pathway, and then predict new biochemical reactions using a rule-based approach...
November 14, 2017: Nature Communications
https://www.readbyqxmd.com/read/29131816/automated-visualization-of-rule-based-models
#9
John Arul Prakash Sekar, Jose-Juan Tapia, James R Faeder
Frameworks such as BioNetGen, Kappa and Simmune use "reaction rules" to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size...
November 13, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/29125126/improving-grn-re-construction-by-mining-hidden-regulatory-signals
#10
Ming Shi, Weiming Shen, Yanwen Chong, Hong-Qiang Wang
Inferring gene regulatory networks (GRNs) from gene expression data is an important but challenging issue in systems biology. Here, the authors propose a dictionary learning-based approach that aims to infer GRNs by globally mining regulatory signals, known or latent. Gene expression is often regulated by various regulatory factors, some of which are observed and some of which are latent. The authors assume that all regulators are unknown for a target gene and the expression of the target gene can be mapped into a regulatory space spanned by all the regulators...
December 2017: IET Systems Biology
https://www.readbyqxmd.com/read/29122012/drug-target-ontology-to-classify-and-integrate-drug-discovery-data
#11
Yu Lin, Saurabh Mehta, Hande Küçük-McGinty, John Paul Turner, Dusica Vidovic, Michele Forlin, Amar Koleti, Dac-Trung Nguyen, Lars Juhl Jensen, Rajarshi Guha, Stephen L Mathias, Oleg Ursu, Vasileios Stathias, Jianbin Duan, Nooshin Nabizadeh, Caty Chung, Christopher Mader, Ubbo Visser, Jeremy J Yang, Cristian G Bologa, Tudor I Oprea, Stephan C Schürer
BACKGROUND: One of the most successful approaches to develop new small molecule therapeutics has been to start from a validated druggable protein target. However, only a small subset of potentially druggable targets has attracted significant research and development resources. The Illuminating the Druggable Genome (IDG) project develops resources to catalyze the development of likely targetable, yet currently understudied prospective drug targets. A central component of the IDG program is a comprehensive knowledge resource of the druggable genome...
November 9, 2017: Journal of Biomedical Semantics
https://www.readbyqxmd.com/read/29120828/the-efficacy-of-respondent-driven-sampling-for-the-health-assessment-of-minority-populations
#12
Grazyna Badowski, Lilnabeth P Somera, Brayan Simsiman, Hye-Ryeon Lee, Kevin Cassel, Alisha Yamanaka, JunHao Ren
BACKGROUND: Respondent driven sampling (RDS) is a relatively new network sampling technique typically employed for hard-to-reach populations. Like snowball sampling, initial respondents or "seeds" recruit additional respondents from their network of friends. Under certain assumptions, the method promises to produce a sample independent from the biases that may have been introduced by the non-random choice of "seeds." We conducted a survey on health communication in Guam's general population using the RDS method, the first survey that has utilized this methodology in Guam...
October 2017: Cancer Epidemiology
https://www.readbyqxmd.com/read/29118696/a-theory-of-how-columns-in-the-neocortex-enable-learning-the-structure-of-the-world
#13
Jeff Hawkins, Subutai Ahmad, Yuwei Cui
Neocortical regions are organized into columns and layers. Connections between layers run mostly perpendicular to the surface suggesting a columnar functional organization. Some layers have long-range excitatory lateral connections suggesting interactions between columns. Similar patterns of connectivity exist in all regions but their exact role remain a mystery. In this paper, we propose a network model composed of columns and layers that performs robust object learning and recognition. Each column integrates its changing input over time to learn complete predictive models of observed objects...
2017: Frontiers in Neural Circuits
https://www.readbyqxmd.com/read/29118406/single-cell-co-expression-subnetwork-analysis
#14
Thomas E Bartlett, Sören Müller, Aaron Diaz
Single-cell transcriptomic data have rapidly become very popular in genomic science. Genomic science also has a long history of using network models to understand the way in which genes work together to carry out specific biological functions. However, working with single-cell data presents major challenges, such as zero inflation and technical noise. These challenges require methods to be specifically adapted to the context of single-cell data. Recently, much effort has been made to develop the theory behind statistical network models...
November 8, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29117534/building-predictive-models-of-genetic-circuits-using-the-principle-of-maximum-caliber
#15
Taylor Firman, Gábor Balázsi, Kingshuk Ghosh
Learning the underlying details of a gene network is a major challenge in cellular and synthetic biology. We address this challenge by building a chemical kinetic model that utilizes information encoded in the stochastic protein expression trajectories typically measured in experiments. The applicability of the proposed method is demonstrated in an auto-activating genetic circuit, a common motif in natural and synthetic gene networks. Our approach is based on the principle of maximum caliber (MaxCal)-a dynamical analog of the principle of maximum entropy-and builds a minimal model using only three constraints: 1) protein synthesis, 2) protein degradation, and 3) positive feedback...
November 7, 2017: Biophysical Journal
https://www.readbyqxmd.com/read/29113310/inference-of-time-delayed-gene-regulatory-networks-based-on-dynamic-bayesian-network-hybrid-learning-method
#16
Bin Yu, Jia-Meng Xu, Shan Li, Cheng Chen, Rui-Xin Chen, Lei Wang, Yan Zhang, Ming-Hui Wang
Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model...
October 6, 2017: Oncotarget
https://www.readbyqxmd.com/read/29111409/mulan-evaluation-and-ensemble-statistical-inference-for-functional-connectivity
#17
Huifang E Wang, Karl J Friston, Christian G Bénar, Marmaduke M Woodman, Patrick Chauvel, Viktor Jirsa, Christophe Bernard
Many analysis methods exist to extract graphs of functional connectivity from neuronal networks. Confidence in the results is limited because, (i) different methods give different results, (ii) parameter setting directly influences the final result, and (iii) systematic evaluation of the results is not always performed. Here, we introduce MULAN (MULtiple method ANalysis), which assumes an ensemble based approach combining multiple analysis methods and fuzzy logic to extract graphs with the most probable structure...
October 27, 2017: NeuroImage
https://www.readbyqxmd.com/read/29102243/tau-amyloid-and-cascading-network-failure-across-the-alzheimer-s-disease-spectrum
#18
David T Jones, Jonathan Graff-Radford, Val J Lowe, Heather J Wiste, Jeffrey L Gunter, Matthew L Senjem, Hugo Botha, Kejal Kantarci, Bradley F Boeve, David S Knopman, Ronald C Petersen, Clifford R Jack
Functionally related brain regions are selectively vulnerable to Alzheimer's disease pathophysiology. However, molecular markers of this pathophysiology (i.e., beta-amyloid and tau aggregates) have discrepant spatial and temporal patterns of progression within these selectively vulnerable brain regions. Existing reductionist pathophysiologic models cannot account for these large-scale spatiotemporal inconsistencies. Within the framework of the recently proposed cascading network failure model of Alzheimer's disease, however, these large-scale patterns are to be expected...
October 3, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/29101378/a-deep-ensemble-model-to-predict-mirna-disease-association
#19
Laiyi Fu, Qinke Peng
Cumulative evidence from biological experiments has confirmed that microRNAs (miRNAs) are related to many types of human diseases through different biological processes. It is anticipated that precise miRNA-disease association prediction could not only help infer potential disease-related miRNA but also boost human diagnosis and disease prevention. Considering the limitations of previous computational models, a more effective computational model needs to be implemented to predict miRNA-disease associations...
November 3, 2017: Scientific Reports
https://www.readbyqxmd.com/read/29096203/modular-representation-of-layered-neural-networks
#20
Chihiro Watanabe, Kaoru Hiramatsu, Kunio Kashino
Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood...
October 12, 2017: Neural Networks: the Official Journal of the International Neural Network Society
keyword
keyword
120107
1
2
Fetch more papers »
Fetching more papers... Fetching...
Read by QxMD. Sign in or create an account to discover new knowledge that matter to you.
Remove bar
Read by QxMD icon Read
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"