Recently, nonlinear vector autoregressive (NVAR) model based on Granger causality was proposed to infer nonlinear gene regulatory networks from time series gene expression data. Since NVAR requires a large number of parameters due to the basis expansion, the length of time series microarray data is insufficient for accurate parameter estimation and we need to limit the size of the gene set strongly. To address this limitation, we employ L1 regularization technique to estimate NVAR. Under L1 regularization, direct parents of each gene can be selected efficiently even when the number of parameters exceeds the number of data samples. We can thus estimate larger gene regulatory networks more accurately than those from existing methods. Through ...
Abstract Background Biological networks are constantly subjected to random perturbations, and effici...
Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on...
International audienceMOTIVATION: Reverse engineering of gene regulatory networks remains a central ...
Abstract Background To understand the molecular mecha...
<div><p>Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the f...
Biological networks have arisen as an attractive paradigm of genomic science ever since the introduc...
The construction of genetic regulatory networks from time series gene expression data is an importan...
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statist...
Dynamic gene-regulatory networks are complex since the interaction patterns between their components...
This paper considers the problem of inferring gene regula-tory networks using time series data. A no...
It is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from tim...
Gene regulatory networks represent an abstract mapping of gene regulations in living cells. They aim...
International audienceReconstructing gene regulatory networks from high-throughput measurements repr...
We present a method for gene network inference and revision based on time-series data. Gene networks...
Components of biological systems interact with each other in order to carry out vital cell functions...
Abstract Background Biological networks are constantly subjected to random perturbations, and effici...
Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on...
International audienceMOTIVATION: Reverse engineering of gene regulatory networks remains a central ...
Abstract Background To understand the molecular mecha...
<div><p>Comprehensive understanding of gene regulatory networks (GRNs) is a major challenge in the f...
Biological networks have arisen as an attractive paradigm of genomic science ever since the introduc...
The construction of genetic regulatory networks from time series gene expression data is an importan...
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statist...
Dynamic gene-regulatory networks are complex since the interaction patterns between their components...
This paper considers the problem of inferring gene regula-tory networks using time series data. A no...
It is of increasing interest in systems biology to discover gene regulatory networks (GRNs) from tim...
Gene regulatory networks represent an abstract mapping of gene regulations in living cells. They aim...
International audienceReconstructing gene regulatory networks from high-throughput measurements repr...
We present a method for gene network inference and revision based on time-series data. Gene networks...
Components of biological systems interact with each other in order to carry out vital cell functions...
Abstract Background Biological networks are constantly subjected to random perturbations, and effici...
Background: In the analysis of effects by cell treatment such as drug dosing, identifying changes on...
International audienceMOTIVATION: Reverse engineering of gene regulatory networks remains a central ...