Method
Towards accurate imputation of quantitative genetic interactions
-
* Corresponding author: Ron Shamir rshamir@tau.ac.il
1 Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel
2 Department of Cellular and Molecular Pharmacology, University of California San Francisco, San Francisco, CA 94158, USA
3 Current address: Whitehead Institute for Biomedical Research, 9 Cambridge Center, Cambridge, MA 02142, USA
Genome Biology 2009, 10:R140 doi:10.1186/gb-2009-10-12-r140
Published: 10 December 2009Additional files
Additional file 1:
Text S1: a proposed explanation for the results of the comparison of the random and the submatrix models. Figure S1: comparison of the linear regression coefficients of GSG features. Figure S2: performance and the number of GSG features. Figure S3: accuracy of prediction of quantitative GIs on the ER and RNA E-MAPs. Figure S4: accuracy of positive GI prediction as a function of positive GI definition. Figure S5: performance using each feature group separately. Figure S6: the effect of missing value imputation on correlation with functional similarity measured using the Wang method. Figure S7: construction of GSG and GSG-MATRIX features. Table S1: correlation between all the features used in this study and the measured S-scores.
Format: DOC Size: 1019KB Download file
This file can be viewed with: Microsoft Word Viewer
Additional file 2:
S-scores in ChromBio, ER and RNA E-MAPs after imputation of missing values.
Format: ZIP Size: 3.7MB Download file