Accurate prediction of protein function and interactions from diverse genomic data is a key problem in systems biology. Heterogeneous data integration remains a challenge, particularly due to noisy data sources, diversity of coverage, and functional biases. It is thus important to understand the behavior and robustness of data integration methods in the context of various biological functions. We focus on the ability of Bayesian networks to predict functional relationships between proteins under a variety of conditions. This study considers the effect of network structure and compares expert estimated conditional probabilities with those learned using a generative method (expectation maximization) and a discriminative method (extended logistic regression). We consider the contributions of individual data sources and interpret these results both globally and in the context of specific biological processes. We find that it is critical to consider variation across biological functions; even when global performance is strong, some categories are consistently predicted well, and others are difficult to analyze. All learned models outperform the equivalent expert estimated models, although this effect diminishes as the amount of available data decreases. These learning techniques are not specific to Bayesian networks, and thus our conclusions should generalize to other methods for data integration. Overall, Bayesian learning provides a consistent benefit in data integration, but its performance and the impact of heterogeneous data sources must be interpreted from the perspective of individual functional categories.
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Evidence ID | Analyze ID | Gene/Complex | Systematic Name/Complex Accession | Qualifier | Gene Ontology Term ID | Gene Ontology Term | Aspect | Annotation Extension | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Phenotype | Experiment Type | Experiment Type Category | Mutant Information | Strain Background | Chemical | Details | Reference |
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Evidence ID | Analyze ID | Gene | Gene Systematic Name | Disease Ontology Term | Disease Ontology Term ID | Qualifier | Evidence | Method | Source | Assigned On | Reference |
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Evidence ID | Analyze ID | Regulator | Regulator Systematic Name | Target | Target Systematic Name | Direction | Regulation of | Happens During | Regulator Type | Direction | Regulation Of | Happens During | Method | Evidence | Strain Background | Reference |
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Site | Modification | Modifier | Source | Reference |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Allele | Assay | Annotation | Action | Phenotype | SGA score | P-value | Source | Reference | Note |
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Evidence ID | Analyze ID | Interactor | Interactor Systematic Name | Interactor | Interactor Systematic Name | Assay | Annotation | Action | Modification | Source | Reference | Note |
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Complement ID | Locus ID | Gene | Species | Gene ID | Strain background | Direction | Details | Source | Reference |
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Evidence ID | Analyze ID | Dataset | Description | Keywords | Number of Conditions | Reference |
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