This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM) Genomics
Research
New methods for separating causes from effects in genomics data
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
BMC Genomics 2012, 13(Suppl 8):S22 doi:10.1186/1471-2164-13-S8-S22
Published: 17 December 2012Additional 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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