An information theoretic algorithm that prunes away potentially indirect interactions allows for improved reconstruction of biological networks.
References
Basso, K. et al. Nat. Gen. 37, 382–390 (2005).
Butte, A.J. & Kohane, I.S. Pac. Symp. Biocomput. 2000, 418–429 (2000).
Butte, A.J., Tamayo, P., Slonim, D., Golub, T.R. & Kohane, I.S. Proc. Natl. Acad. Sci. USA 97, 12182–12186 (2000).
Barabasi, A.L. Linked: How Everything Is Connected to Everything Else and What It Means (Plume, New York, NY, 2003).
The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology. Nat. Gen. 25, 25–29 (2000).
Smith, V.A., Jarvis, E.D. & Hartemink, A.J. Bioinformatics 18, S216–S224 (2002).
Yu, J., Smith, V.A., Wang, P.P., Hartemink A.J. & Jarvis, E.D. in 3rd International Conference on Systems Biology (Karolinska Institute, Stockholm, Sweden, 2002).
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Hartemink, A. Reverse engineering gene regulatory networks. Nat Biotechnol 23, 554–555 (2005). https://doi.org/10.1038/nbt0505-554
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DOI: https://doi.org/10.1038/nbt0505-554
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