Optimal design of gene knockout experiments for gene regulatory network inference
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[1] Alfred V. Aho,et al. The Transitive Reduction of a Directed Graph , 1972, SIAM J. Comput..
[2] M. Levandowsky,et al. Distance between Sets , 1971, Nature.
[3] Peter Bühlmann,et al. Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm , 2007, J. Mach. Learn. Res..
[4] Andreas Zell,et al. Iteratively Inferring Gene Regulatory Networks with Virtual Knockout Experiments , 2004, EvoWorkshops.
[5] Sanjeev Khanna,et al. Approximating Longest Directed Paths and Cycles , 2004, ICALP.
[6] Rudiyanto Gunawan,et al. Ensemble Inference and Inferability of Gene Regulatory Networks , 2014, PloS one.
[7] Adilson E Motter,et al. Sub-optimal phenotypes of double-knockout mutants of Escherichia coli depend on the order of gene deletions. , 2015, Integrative biology : quantitative biosciences from nano to macro.
[8] Dawei Hong,et al. A theoretical approach to gene network identification , 2012, 2012 IEEE Information Theory Workshop.
[9] J. Roach,et al. Statistical analysis of MPSS measurements: application to the study of LPS-activated macrophage gene expression. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[10] J. Kato,et al. Construction of consecutive deletions of the Escherichia coli chromosome , 2007, Molecular systems biology.
[11] Albert-László Barabási,et al. Statistical mechanics of complex networks , 2001, ArXiv.
[12] Dario Floreano,et al. GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods , 2011, Bioinform..
[13] Diogo M. Camacho,et al. Wisdom of crowds for robust gene network inference , 2012, Nature Methods.
[14] Florian Steinke,et al. Experimental design for efficient identification of gene regulatory networks using sparse Bayesian models , 2006, BMC Systems Biology.
[15] J. Hasty,et al. Reverse engineering gene networks: Integrating genetic perturbations with dynamical modeling , 2003, Proceedings of the National Academy of Sciences of the United States of America.
[16] Julio R. Banga,et al. Inference of complex biological networks: distinguishability issues and optimization-based solutions , 2011, BMC Systems Biology.
[17] V. Thorsson,et al. Discovery of regulatory interactions through perturbation: inference and experimental design. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.
[18] Moritz Lang,et al. Cutting the wires: modularization of cellular networks for experimental design. , 2014, Biophysical journal.
[19] Peter Bühlmann,et al. Predicting causal effects in large-scale systems from observational data , 2010, Nature Methods.
[20] Julio Saez-Rodriguez,et al. Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge , 2011, Science Signaling.
[21] Cesare Furlanello,et al. A promoter-level mammalian expression atlas , 2015 .
[22] John H. Holland,et al. Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .