• KELLER

  • Referenced in 2 articles [sw22600]
  • time-varying interactions between genes. MOTIVATION: Gene regulatory networks underlying temporal processes, such ... capture the temporal evolution of the regulatory networks. These methods will be an enabling first ... studying the driving forces underlying the dynamic gene regulation circuitry and predicting the future network...
  • BGRMI

  • Referenced in 1 article [sw29320]
  • BGRMI: A method for inferring gene regulatory networks from time-course gene expression data ... application in breast cancer research. Reconstructing gene regulatory networks (GRNs) from gene expression data ... fast but less accurate. We propose Bayesian Gene Regulation Model Inference (BGRMI), a model-based ... regulatory networks of proliferating and differentiating breast cancer (BC) cells from time-course gene expression...
  • SELANSI

  • Referenced in 2 articles [sw28254]
  • simulation of stochastic multidimensional Gene Regulatory Networks (GRNs). SELANSI exploits intrinsic structural properties of GRNs...
  • GENeVis

  • Referenced in 1 article [sw06743]
  • Interactive visualization of gene regulatory networks with associated gene expression time series data We present ... expression time series data in a gene regulatory network context. This is a network ... regulate the expression of their respective target genes. The networks are represented as graphs...
  • bc3net

  • Referenced in 1 article [sw17708]
  • package bc3net: Gene Regulatory Network Inference with Bc3net. Implementation of the BC3NET algorithm for gene ... regulatory network inference (de Matos Simoes and Frank Emmert-Streib, Bagging Statistical Network Inference from ... Large-Scale Gene Expression Data, PLoS ONE 7(3): e33624...
  • PyPanda

  • Referenced in 1 article [sw14828]
  • Regulatory Network Reconstruction. PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory...
  • VirtualLeaf

  • Referenced in 3 articles [sw28766]
  • insight into the roles of gene products in regulatory networks, the conditions of gene expression...
  • NetBenchmark

  • Referenced in 1 article [sw29311]
  • bioconductor package for reproducible benchmarks of gene regulatory network inference. Background: In the last decade ... great number of methods for reconstructing gene regulatory networks from expression data have been proposed...
  • RoVerGeNe

  • Referenced in 13 articles [sw10954]
  • Gene Networks. Rovergene is a tool for the analysis of genetic regulatory networks under parameter ... uncertainty. Genetic regulatory networks are described by a class of piecewise multiaffine differential equation models ... robustness of dynamical properties of gene networks with respect to parameter variations...
  • SPINE

  • Referenced in 5 articles [sw29309]
  • that connect causal to affected genes on a network of physical interactions. Results: We present ... regulatory Pathway INferencE. The framework aims at explaining gene expression experiments in which a gene ... gene to the affected genes are searched for in this network. The reconstruction problem ... entire yeast network to predict protein effects and reconstruct signaling and regulatory pathways. Overall...
  • dynGENIE3

  • Referenced in 1 article [sw29312]
  • series expression data. The elucidation of gene regulatory networks is one of the major challenges...
  • SCENIC

  • Referenced in 1 article [sw29313]
  • SCENIC, a computational method for simultaneous gene regulatory network reconstruction and cell-state identification from...
  • GeneSPIDER

  • Referenced in 1 article [sw29318]
  • GeneSPIDER - gene regulatory network inference benchmarking with controlled network and data properties. A key question...
  • SINCERITIES

  • Referenced in 1 article [sw29321]
  • SINCERITIES is a tool for inferring gene regulatory networks from time-stamped cross-sectional single ... particular, SINCERITIES recovers the causal relationships among genes by analyzing the evolution of the distribution...
  • CSI

  • Referenced in 1 article [sw13904]
  • Bayesian approach to network inference from multiple perturbed time series gene expression data. Here ... Gaussian process based approach to inferring gene regulatory networks (GRNs) from multiple time series data...
  • TRaCE+

  • Referenced in 1 article [sw29319]
  • Closure Ensemble. Inferring the structure of gene regulatory networks (GRNs) from expression data ... network inference is often stated to be underdetermined. The underdetermined nature of the network inference ... network solutions to the inference problem, i.e. the solution consists of an ensemble of networks ... defines the set of uncertain edges, representing gene regulations that could not be verified...
  • ICEP

  • Referenced in 1 article [sw23747]
  • understanding of complex relationships in gene regulatory networks. However, the complexity of array design, production...
  • ConsensusPathDB

  • Referenced in 6 articles [sw29342]
  • reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously ... describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With 155 432 human...
  • pandaR

  • Referenced in 0 articles [sw19414]
  • Estimating Gene Regulatory Networks with pandaR. PANDA (Passing Attributes betweenNetworks forData Assimilation) is a gene ... regulatory network inference method that begins with amodel of transcription factor-target gene interactions ... networks for each experimental group and the network models are then compared between groups ... framework for exploratory data analysis on gene regulatory networks...
  • HPAM

  • Referenced in 3 articles [sw10931]
  • identifying regulatory systems and integrating them into genetic networks. Gene expression is determined by protein ... regulatory factors operating in the transcription of a gene, becomes crucial for determining which genes ... they are connected to build genetic networks. We propose a hybrid promoter analysis methodology (HPAM ... complex promoter motifs that combines the neural network efficiency and ability of representing imprecise...