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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM) – Genomics

Open Access Open Badges Research

New methods for separating causes from effects in genomics data

Alexander Statnikov12*, Mikael Henaff1, Nikita I Lytkin1 and Constantin F Aliferis134

Author Affiliations

1 Center for Health Informatics and Bioinformatics, New York University Langone Medical Center, New York, NY 10016, USA

2 Department of Medicine, Division of Translational Medicine, New York University School of Medicine, New York, NY 10016, USA

3 Department of Pathology, New York University School of Medicine, New York, NY 10016, USA

4 Department of Biostatistics, Vanderbilt University, Nashville, TN, 37232, USA

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BMC Genomics 2012, 13(Suppl 8):S22  doi:10.1186/1471-2164-13-S8-S22

Published: 17 December 2012

Additional files

Additional file 1:

This file contains (1) brief description of causal orientation algorithms; (2) results of causal orientation methods ANM, PNL, and GPI obtained by assessing statistical significance of the forward and backward causal models; (3) detailed results of significance testing of IGCI Gaussian/Entropy and Gaussian/Integral methods; (4) explanation of performance increase due to adding small amount of noise or reducing the sample size in YEAST gold standard.

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