BMC Genomics 2008, 9:461 http://www.biomedcentral.com/1471-2164/9/461 itively, a correlation based on a dataset having many sam- ples may be more significant than the same value derived from another dataset with only a few samples. If there are n datasets, and each reports a correlation of xi for a gene pair, the final correlation value is computed by corr = Σi(xi * si)/Σi(si) where i ∈ [1, 2, ..., n], and si represents the number of sam- ples in data set i. Every interaction in DroID is annotated with the current gene expression correlation value for that pair. Correlation values are updateable as new gene expression datasets are added to GEO. Cross data set confidence scores Protein-protein interaction data tend to be noisy, with variable rates of false positives from one dataset to another. A novel feature of DroID is the annotation of each physical protein-protein interaction with an update- able confidence score that reflects the probability that it is a biologically relevant true positive. Most methods for generating confidence scores work within a single type of data, such as yeast two-hybrid or protein complex data, by searching for features of the data that correlate with bio- logical significance [30,44-47]. As a consequence, the scores derived for one data set bear little relation to those for another data set. In contrast, DroID assigns confidence scores to all physical interactions, including data from dif- ferent techniques and interologs derived from worm, yeast, and human. The method used to assign confidence scores is based on the logistic regression approach described by Giot et al. [30,44]. In this approach we first identify training data, including a set of interactions that are likely to be true positives and another set that are likely to be false positives. We then search for specific attributes of the interactions that correlate with the two training sets. The attributes include gene expression correlation, number of associated literature citations (PubMed identi- fiers or PMIDs), local and global network topology, and domain-domain interactions. For example, the number of PMIDs for an interaction correlates with its likelihood of being in the true positive training set. Conversely, the number of interactions for a protein is inversely correlated Table 2: Overlap between interaction datasets with presence in the true positive training set. The correla- tions are then used to train a logistic regression model that can assign scores to all interactions based on their attributes. For the interactions in DroID, we used a varia- tion of this scoring system in which we combine multiple training datasets to reduce the potential bias of any single training set (Yu, submitted). Every physical interaction in DroID has a confidence score between 0 and 1 to represent the probability that it is a biological true positive. Validation of the scoring system shows that interactions with higher scores are more likely to be biologically relevant than interactions with lower scores (Yu et al., submitted; [44]). The set of interactions with scores greater than 0.5, for example, have signifi- cantly more pairs of genes that share GO biological proc- ess or cellular component annotations compared to interactions scoring < 0.5, or to random pairs. Interactions scoring less than 0.5 also share significantly more GO annotations than random pairs of genes, which indicates that overall the interactions collected in DroID are enriched for biologically relevant true positives. As addi- tional interaction data and other new information become available, the scoring models can be periodically retrained to improve the overall accuracy. Thus, the confi- dence scores are updateable and receive a version number at each revision. Utility and discussion Summary of the database The current version of DroID (v4.0) contains 131,659 links among 9,511 D. melanogaster genes, or roughly 64.4% of the predicted genes. The small amount of over- lap between different interaction sets (Table 2) shows that no single dataset adequately covers the available data, and serves to illustrate the value of making all data available in one location. A major limitation to the value of most interaction datasets is the presence of false positive inter- actions that have no biological significance. To overcome this limitation and to help biologists focus on the most reliable interactions, DroID assigns confidence scores to individual interactions to denote potential biological sig- nificance. The current version of the confidence scoring Finley Curagen Hybrigenics Other* Genetic Human Yeast Worm 14 69 8 12 8 201 137 Yeast 73 168 15 52 55 4830 Human Genetic Other* Hybrigenics Curagen 118 263 54 220 403 15 35 24 182 6 48 9 4 23 48 * Physical interactions that were detected outside the three large-scale yeast two-hybrid screens. Page 5 of 9 (page number not for citation purposes)