A review of active learning approaches to experimental design for uncovering biological networks

Optimal design of gene knockout experiments for gene regulatory network inference

(2015) S. M. Minhaz Ud-Dean et al.   BIOINFORMATICS

Optimal Experimental Design for Gene Regulatory Networks in the Presence of Uncertainty

(2015) Roozbeh Dehghannasiri et al.   IEEE-ACM Transactions on Computational Biology and Bioinformatics

Experimental design schemes for learning Boolean network models

(2014) N. Atias et al.   BIOINFORMATICS

Joint estimation of causal effects from observational and intervention gene expression data

(2013) Andrea Rau et al.   BMC Systems Biology

Reconstructing Boolean Models of Signaling

(2013) Roded Sharan et al.   JOURNAL OF COMPUTATIONAL BIOLOGY

Learning Bayesian networks: approaches and issues

(2011) Rónán Daly et al.   KNOWLEDGE ENGINEERING REVIEW

Large-scale network models of IL-1 and IL-6 signalling and their hepatocellular specification

(2011) Anke Ryll et al.   Molecular BioSystems

Discriminating between rival biochemical network models: three approaches to optimal experiment design

(2010) Bence Mélykúti et al.   BMC Systems Biology

Predicting causal effects in large-scale systems from observational data

(2010) Marloes H Maathuis et al.   NATURE METHODS

The Automation of Science

(2009) Ross D. King et al.   SCIENCE

The Logic of EGFR/ErbB Signaling: Theoretical Properties and Analysis of High-Throughput Data

(2009) Regina Samaga et al.   PLoS Computational Biology

Robust Intervention in Probabilistic Boolean Networks

(2008) R. Pal et al.   IEEE TRANSACTIONS ON SIGNAL PROCESSING