Integrative random forest for gene regulatory network inference

Performance and Improvement of Tree-Based Methods for Gene Regulatory Network Reconstruction

Computational reconstruction of gene regulatory networks (GRNs) from gene expression data is of great importance in systems biology. Dialogue for Reverse Engineering Assessments and Methods (DREAM) challenge aims to evaluate the success of computational GRN inference algorithm on benchmarks of simulated data. Tree-based methods, such as Random Forest, infer true regulators of a target gene in a...

From genetics to gene networks: Evaluating approaches for integrative analysis of genetic marker and gene expression data for the purpose of gene network inference

A Learning Framework to Improve Unsupervised Gene Network Inference

Network inference through link prediction is an important data mining problem that finds many applications in computational social science and biomedicine. For example, by predicting links, i.e., regulatory relationships, between genes to infer gene regulatory networks (GRNs), computational biologists gain a better understanding of the functional elements and regulatory circuits in cells. Unsup...

Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks.

Gaining insights on gene regulation from large-scale functional data sets is a grand challenge in systems biology. In this article, we develop and apply methods for transcriptional regulatory network inference from diverse functional genomics data sets and demonstrate their value for gene function and gene expression prediction. We formulate the network inference problem in a machine-learning f...

Supplementary Information Predictive regulatory models in Drosophila melanogaster by integrative inference of transcriptional networks

S1 Fraction of integrative network edges with and without physical support . . . . . 4 S2 Degree distributions of integrative networks . . . . . . . . . . . . . . . . . . . . . 5 S3 Structural properties of integrative networks . . . . . . . . . . . . . . . . . . . . . 6 S4 Network motifs of integrative networks . . . . . . . . . . . . . . . . . . . . . . . . 7 S5 Prediction of novel functional...