Supervised, semi-supervised and unsupervised inference of gene regulatory networks

PLAU inferred from a correlation network is critical for suppressor function of regulatory T cells

(2012) Feng He et al.   Molecular Systems Biology

Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL

(2012) Giusy Della Gatta et al.   NATURE MEDICINE

Wisdom of crowds for robust gene network inference

(2012) Daniel Marbach et al.   NATURE METHODS

Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets

(2012) Piyush B Madhamshettiwar et al.   Genome Medicine

GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods

(2011) Thomas Schaffter et al.   BIOINFORMATICS

Principles and Strategies for Developing Network Models in Cancer

(2011) Dana Pe'er et al.   CELL

Detecting Novel Associations in Large Data Sets

(2011) D. N. Reshef et al.   SCIENCE

Revealing differences in gene network inference algorithms on the network level by ensemble methods

(2010) Gökmen Altay et al.   BIOINFORMATICS

Learning gene regulatory networks from only positive and unlabeled data

(2010) Luigi Cerulo et al.   BMC BIOINFORMATICS

Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges

(2010) Robert J. Prill et al.   PLoS One

Inferring Regulatory Networks from Expression Data Using Tree-Based Methods

(2010) Vân Anh Huynh-Thu et al.   PLoS One

Revealing strengths and weaknesses of methods for gene network inference

(2010) D. Marbach et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Lessons from the DREAM2 Challenges

(2009) Gustavo Stolovitzky et al.   Annals of the New York Academy of Sciences

Benchmarking regulatory network reconstruction with GRENDEL

(2009) B. C. Haynes et al.   BIOINFORMATICS

A Yeast Synthetic Network for In Vivo Assessment of Reverse-Engineering and Modeling Approaches

(2009) Irene Cantone et al.   CELL

Rank of Correlation Coefficient as a Comparable Measure for Biological Significance of Gene Coexpression

(2009) T. Obayashi et al.   DNA RESEARCH

Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

(2009) Daniel Marbach et al.   JOURNAL OF COMPUTATIONAL BIOLOGY

SIRENE: supervised inference of regulatory networks

(2008) F. Mordelet et al.   BIOINFORMATICS

Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

(2008) Antonio Reverter et al.   BIOINFORMATICS

minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information

(2008) Patrick E Meyer et al.   BMC BIOINFORMATICS

WGCNA: an R package for weighted correlation network analysis

(2008) Peter Langfelder et al.   BMC BIOINFORMATICS