Abstract
Over the past decade gene expression data sets have been generated at an increasing pace. In addition to ever increasing data generation, the biomedical literature is growing exponentially. The PubMed database (Sayers et al., 2010) comprises more than 20 million citations as of October 2010. The goal of our method is the prediction of putative upstream regulators of observed expression changes based on a set of over 400,000 causal relationships. The resulting putative regulators constitute directly testable hypotheses for follow-up.
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Chindelevitch, L. et al. (2011). Causal Reasoning on Biological Networks: Interpreting Transcriptional Changes. In: Bafna, V., Sahinalp, S.C. (eds) Research in Computational Molecular Biology. RECOMB 2011. Lecture Notes in Computer Science(), vol 6577. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20036-6_4
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DOI: https://doi.org/10.1007/978-3-642-20036-6_4
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