Gene regulatory network inference is essential to uncover complex relationships among gene pathways and inform downstream experiments, ultimately enabling regulatory network re-engineering. Network inference from transcriptional time-series data requires accurate, interpretable, and efficient determination of causal relationships among thousands of genes. Here, we develop Bootstrap Elastic net regression from Time Series (BETS), a statistical framework based on Granger causality for the recovery of a directed gene network from transcriptional time-series data. BETS uses elastic net regression and stability selection from bootstrapped samples to infer causal relationships among genes. BETS is highly parallelized, enabling efficient analysis ...
Over the last few decades, many genes have been functionally characterized and shown to be involved ...
BACKGROUND: Reverse-engineering gene networks from expression profiles is a difficult problem for wh...
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intric...
Gene regulatory network inference is essential to uncover complex relationships among gene pathways ...
Gene regulatory network inference is essential to uncover complex relationships among gene pathways ...
Gene regulatory network inference is essential to uncover complex relationships among gene pathways ...
Gene regulatory network inference is essential to uncover complex relationships among gene pathways ...
The inference of regulatory and biochemical networks from large-scale genomics data is a basic probl...
We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory n...
We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory n...
Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Man...
The discovery of gene regulatory network (GRN) using gene expression data is one of the promising di...
Abstract We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by in...
Motivation: Prior biological knowledge greatly facilitates the mean-ingful interpretation of gene-ex...
Over the last few decades, many genes have been functionally characterized and shown to be involved ...
Over the last few decades, many genes have been functionally characterized and shown to be involved ...
BACKGROUND: Reverse-engineering gene networks from expression profiles is a difficult problem for wh...
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intric...
Gene regulatory network inference is essential to uncover complex relationships among gene pathways ...
Gene regulatory network inference is essential to uncover complex relationships among gene pathways ...
Gene regulatory network inference is essential to uncover complex relationships among gene pathways ...
Gene regulatory network inference is essential to uncover complex relationships among gene pathways ...
The inference of regulatory and biochemical networks from large-scale genomics data is a basic probl...
We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory n...
We introduce a novel method called Prophetic Granger Causality (PGC) for inferring gene regulatory n...
Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Man...
The discovery of gene regulatory network (GRN) using gene expression data is one of the promising di...
Abstract We present IDEA (the Induction Dynamics gene Expression Atlas), a dataset constructed by in...
Motivation: Prior biological knowledge greatly facilitates the mean-ingful interpretation of gene-ex...
Over the last few decades, many genes have been functionally characterized and shown to be involved ...
Over the last few decades, many genes have been functionally characterized and shown to be involved ...
BACKGROUND: Reverse-engineering gene networks from expression profiles is a difficult problem for wh...
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intric...