See Text S1 for details regarding all statistical assessments performed, including details about our normality assumption (Determine S7). The Ste level expressed by a genetic population was defined as , where and are the brood size measurements for and wild-type animals, respectively. predicted interactions, stratified by gene characterization index (see Text S1). Our approach predicts novel interactions for genes orthologous to poorly-characterized human genes.(0.42 MB TIF) pone.0010624.s003.tif (413K) GUID:?D39B9103-868F-4174-AD67-61D8A0C28DAB Figure S3: The relationship between the quantity of information available for a gene and the number of predicted genetic interactions. The quantity of information available for a gene is usually a measure that takes into account the fact that some gene pair attributes are more useful than others for predicting genetic interactions. See the Methods for the computation of the total quantity of information for each gene. MRSP: mental retardation and synaptic plasticity; ZS: Zhong and Sternberg . (A) The total quantity of information available for MRSP genes with the ZS approach and with our approach. The three sets of boxplots correspond to MRSP Rocuronium genes with predicted interactions in this study only, in the ZS study only and in neither study, respectively. (B) Types and total quantity of information available for MRSP genes Rocuronium with the ZS approach and with our approach. Each column corresponds to a gene and a black entry indicates that there is information for the gene of the type specified (to the left) by the row (except for the row labeled Total quantity of information). The ZS approach separates the information from three organisms: (((is usually highlighted in green.(1.22 MB TIF) pone.0010624.s004.tif (1.1M) GUID:?AD7AA379-F221-4406-87BB-4C0BAB717C58 Figure S4: Different methods for estimating the value associated with a Pearson correlation value measuring the coexpression of two genes in the Kim dataset . The grey bars indicate the empirical values associated with bins of correlation values. The transform (red line) methods do not produce values that match the empirical trend closely. In contrast, the fitted normal distribution approximates the empirical distribution well (green line).(0.14 MB TIF) pone.0010624.s005.tif (137K) GUID:?AC8C1EA5-133A-4DE3-A12B-B7B3C353E652 Physique S5: The dependencies between the predictive gene pair attributes as defined by a learned Bayesian network. Rocuronium See the Methods for how the Bayesian network was derived.(0.13 MB TIF) pone.0010624.s006.tif (124K) GUID:?28061990-42EA-48FD-89EC-3BC141C36431 Physique S6: The interaction of with Rocuronium unbalanced heterozygotes of heterozygotes (+/?), submitted to either or RNAi, is usually shown. The error bars correspond to one standard error over three impartial experiments. (*) indicates a statistical difference between and +/? animals submitted to (treatment (see Text S1). Each red line is usually a fitted normal distribution.(0.11 MB TIF) pone.0010624.s008.tif (105K) GUID:?78EB4AB2-E2D5-4335-A428-735803647DAB Table S1: Genetic interactions hand-curated from the literature.(0.05 MB XLS) pone.0010624.s009.xls (45K) GUID:?8093F106-0049-46B1-969E-3E43BF2AFA9A Table S2: Performance of genetic interaction predictors.(0.02 MB XLS) pone.0010624.s010.xls (19K) GUID:?AC295847-421A-48BD-9397-F24CC57F79F1 Table S3: Signaling pathway genes curated from the literature.(0.05 MB XLS) pone.0010624.s011.xls (48K) GUID:?C045AC5C-D11C-4AD8-AB20-444B65E4F86A Table S4: Curated set Rocuronium of mental retardation and synaptic plasticity genes and their orthologues (204 genes).(0.04 MB XLS) pone.0010624.s012.xls (43K) GUID:?7174A399-E774-4F9A-8FD0-C2EC42F6EDDE Table S5: Epistasis coefficients of experimentally tested genetic interactions.(0.03 MB XLS) pone.0010624.s013.xls (26K) GUID:?E70651D1-04F6-4F6C-A793-1D66C9B4364C Table S6: Epistasis values of experimentally tested genetic interactions.(0.02 MB XLS) pone.0010624.s014.xls (24K) GUID:?7DF6AA0D-9DAA-4A2A-B557-49BB14C2CE89 Table S7: AIC values of 63 logistic regression models that use different combinations of the gene pair attributes.(0.03 MB XLS) pone.0010624.s015.xls (30K) GUID:?6E7A6611-AFD1-4EC2-A360-47F65B5CA3BB Table S8: Genotypes of strains used in this study.(0.02 MB XLS) pone.0010624.s016.xls (20K) GUID:?DBF439F9-650F-4249-80C1-89FFEA524FA5 Abstract Background The symptoms of numerous diseases result from genetic mutations that disrupt the homeostasis maintained by the appropriate integration of signaling gene activities. The relationships between signaling genes suggest avenues through which homeostasis can be restored and disease Lyl-1 antibody symptoms subsequently reduced. Specifically, disease symptoms caused by loss-of-function mutations in a particular gene may be reduced by concomitant perturbations in genes with antagonistic activities. Methodology/Principal Findings Here we use network-neighborhood analyses to predict genetic interactions in towards mapping antagonisms and synergisms between genes in an animal model. Most of the predicted interactions are novel, and.