Network deconvolution as a general method to distinguish direct dependencies in networks
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Interaction between IGFBP7 and insulin: a theoretical and experimental studyBRANE Cut: biologically-related a priori network enhancement with graph cuts for gene regulatory network inference.Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for BiologistsQuantitative and logic modelling of molecular and gene networksMIDER: network inference with mutual information distance and entropy reductionInference of Gene Regulatory Network Based on Local Bayesian NetworksGeographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern ChinaImproving accuracy of protein contact prediction using balanced network deconvolution.Identifying binary protein-protein interactions from affinity purification mass spectrometry data.High-throughput single-cell analysis for the proteomic dynamics study of the yeast osmotic stress responseNetwork inference from AP-MS data: computational challenges and solutions.Inferring dynamic gene regulatory networks in cardiac differentiation through the integration of multi-dimensional data.Genomic data assimilation using a higher moment filtering technique for restoration of gene regulatory networks.Gene regulatory network inference using fused LASSO on multiple data setsDifferential network analysis from cross-platform gene expression data.Identifying differential networks based on multi-platform gene expression data.Significant Impacts of Increasing Aridity on the Arid Soil Microbiome.Paradoxical results in perturbation-based signaling network reconstructionA Robust Method for Inferring Network StructuresUsing random walks to generate associations between objects.Large differences in global transcriptional regulatory programs of normal and tumor colon cellsTumor evolutionary directed graphs and the history of chronic lymphocytic leukemiaInferring the temporal order of cancer gene mutations in individual tumor samplesStability indicators in network reconstruction.Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks.Highly sensitive inference of time-delayed gene regulation by network deconvolution.Comparative study of the effectiveness and limitations of current methods for detecting sequence coevolutionNetter: re-ranking gene network inference predictions using structural network propertiesStructure-based Markov random field model for representing evolutionary constraints on functional sites.Discriminating direct and indirect connectivities in biological networks.Networks' Characteristics Matter for Systems BiologyStructure-Function Network Mapping and Its Assessment via Persistent HomologyComputational Analysis of Residue Interaction Networks and Coevolutionary Relationships in the Hsp70 Chaperones: A Community-Hopping Model of Allosteric Regulation and CommunicationDMirNet: Inferring direct microRNA-mRNA association networks.VCNet: Vector based gene Co-expression Network construction and its application to RNA-seq data.Universal data-based method for reconstructing complex networks with binary-state dynamics.Inferring sparse networks for noisy transient processesRecursive Indirect-Paths Modularity (RIP-M) for Detecting Community Structure in RNA-Seq Co-expression Networks.PHOCOS: inferring multi-feature phenotypic crosstalk networks.Part mutual information for quantifying direct associations in networks
P2860
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P2860
Network deconvolution as a general method to distinguish direct dependencies in networks
description
2013 nî lūn-bûn
@nan
2013 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
Network deconvolution as a general method to distinguish direct dependencies in networks
@ast
Network deconvolution as a general method to distinguish direct dependencies in networks
@en
type
label
Network deconvolution as a general method to distinguish direct dependencies in networks
@ast
Network deconvolution as a general method to distinguish direct dependencies in networks
@en
prefLabel
Network deconvolution as a general method to distinguish direct dependencies in networks
@ast
Network deconvolution as a general method to distinguish direct dependencies in networks
@en
P2093
P2860
P356
P1433
P1476
Network deconvolution as a general method to distinguish direct dependencies in networks
@en
P2093
Daniel Marbach
Muriel Médard
Soheil Feizi
P2860
P2888
P304
P356
10.1038/NBT.2635
P577
2013-07-14T00:00:00Z