Generating realistic in silico gene networks for performance assessment of reverse engineering methods.
about
Reconstruction of gene co-expression network from microarray data using local expression patternsWisdom of crowds for robust gene network inferenceGUIdock: Using Docker Containers with a Common Graphics User Interface to Address the Reproducibility of Research.Crowdsourcing biomedical research: leveraging communities as innovation enginesNetwork Inference and Biological DynamicsNetwork reconstruction using nonparametric additive ODE modelsMIDER: network inference with mutual information distance and entropy reductionInference of gene regulatory networks incorporating multi-source biological knowledge via a state space model with L1 regularizationLarge scale gene regulatory network inference with a multi-level strategy.A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks.ENNET: inferring large gene regulatory networks from expression data using gradient boostingJoint estimation of causal effects from observational and intervention gene expression dataInferring nonlinear gene regulatory networks from gene expression data based on distance correlation.A Sparse Reconstruction Approach for Identifying Gene Regulatory Networks Using Steady-State Experiment Data.Enabling network inference methods to handle missing data and outliers.DTW-MIC Coexpression Networks from Time-Course Data.Practical aspects of gene regulatory inference via conditional inference forests from expression data.CMIP: a software package capable of reconstructing genome-wide regulatory networks using gene expression dataMapReduce Algorithms for Inferring Gene Regulatory Networks from Time-Series Microarray Data Using an Information-Theoretic Approach.Comparison of evolutionary algorithms in gene regulatory network model inference.Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data.Evolving cell models for systems and synthetic biology.Towards a rigorous assessment of systems biology models: the DREAM3 challengesDREAM3: network inference using dynamic context likelihood of relatedness and the inferelatorLearning gene regulatory networks from only positive and unlabeled data.Petri Nets with Fuzzy Logic (PNFL): reverse engineering and parametrization.Inferring regulatory networks from expression data using tree-based methods.Time lagged information theoretic approaches to the reverse engineering of gene regulatory networksFrom knockouts to networks: establishing direct cause-effect relationships through graph analysis.Inferring gene regression networks with model trees.Inferring gene regulatory networks from asynchronous microarray data with AIRnet.DREAM4: Combining genetic and dynamic information to identify biological networks and dynamical modelsFunctional data analysis for identifying nonlinear models of gene regulatory networksRevealing strengths and weaknesses of methods for gene network inference.Gene regulatory networks from multifactorial perturbations using Graphical Lasso: application to the DREAM4 challenge.A computational framework for gene regulatory network inference that combines multiple methods and datasetsDirected partial correlation: inferring large-scale gene regulatory network through induced topology disruptions.Inference of gene regulatory networks from time series by Tsallis entropySimulating systems genetics data with SysGenSIMA relative variation-based method to unraveling gene regulatory networks.
P2860
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P2860
Generating realistic in silico gene networks for performance assessment of reverse engineering methods.
description
2009 nî lūn-bûn
@nan
2009年の論文
@ja
2009年学术文章
@wuu
2009年学术文章
@zh
2009年学术文章
@zh-cn
2009年学术文章
@zh-hans
2009年学术文章
@zh-my
2009年学术文章
@zh-sg
2009年學術文章
@yue
2009年學術文章
@zh-hant
name
Generating realistic in silico ...... f reverse engineering methods.
@en
Generating realistic in silico ...... f reverse engineering methods.
@nl
type
label
Generating realistic in silico ...... f reverse engineering methods.
@en
Generating realistic in silico ...... f reverse engineering methods.
@nl
prefLabel
Generating realistic in silico ...... f reverse engineering methods.
@en
Generating realistic in silico ...... f reverse engineering methods.
@nl
P2093
P356
P1476
Generating realistic in silico ...... f reverse engineering methods.
@en
P2093
Claudio Mattiussi
Daniel Marbach
Dario Floreano
Thomas Schaffter
P304
P356
10.1089/CMB.2008.09TT
P577
2009-02-01T00:00:00Z