Nucleic Acids Research, 2009, Vol. 37, Web Server issue W143 Figure 1. The results page is composed of several sections. The table of signiﬁcant abstracts (A), here related to microarray and protein aggregation, is sorted by ascending P-values and shows article titles and PubMed identiﬁers (PMIDs). Discriminative words are highlighted in titles or in abstracts, which are displayed in a popup window hyperlinked from their PMID (B). The performance of the ranking is shown using a table and the corresponding Receiver Operating Characteristic curve plotting the sensitivity versus the false positive rate (C). The last section contains the table of discriminative words (D), which is sorted by decreasing weights (the most important words at the top). sets can also be selected just by clicking on hyperlinks. If the user decides to run the analysis with the default parameters, the training set proﬁle will be compared to a precomputed proﬁle of the entire Medline database, and used to rank ten thousand recent abstracts. Beyond the input set, a second main parameter of MedlineRanker is the choice of the reference abstracts, i.e. the background set. To construct a proﬁle for the query topic, the noun frequencies in the training set are compared to the corresponding frequencies in the back- ground set by a linear na ı¨ve Bayesian classiﬁer. The default background set is the entire Medline database, which is clearly suitable when ranking recent abstracts or the most recent years of the literature. We recommend using the default background set, however, one can also provide their own list of PMIDs. This may be useful when the abstracts that have to be ranked are all related to a same secondary topic. For instance, if one is interested to rank abstracts already related to protein binding accord- ing to their relevance for the topic ‘Phosphorylation’, an appropriate background set would be a list of abstracts related to protein binding. The last main parameter deﬁnes which abstracts are going to be ranked, i.e. the test set. By default, 10 000 recent abstracts are selected. By using this relatively small subset of Medline, the results can be returned quickly and the performance of the training set can be evaluated in little time. The test set can be extended to the last months or years of Medline with a cost in compu- tational time. Our server can process approximately one million of abstracts per minute. Alternatively, the user can input his own test set with a list of PMIDs. This is very useful for focusing a search on a particular set of abstracts of interest. For instance, if one was inter- ested in ranking abstracts describing PPI, the main PPI databases, like HPRD, DIP or MINT, provide PMIDs for each described interaction. The results page The results page shows the ranked test set as a table (Figure 1A), with the most relevant records at the top of the table. For each abstract, the table shows the rank, PMID, title and P-value. The discriminative words that were used to score the abstracts are highlighted in the column containing the article title. Clicking on a PMID opens a pop-up window showing the whole abstract text with highlighted discriminative words, further information and a link to PubMed (Figure 1B). During the ranking, a leave-one-out cross validation is done on a subset of the data. This provides an estimation of the method’s predictive performance, including precision and recall, for several cut-oﬀs and is displayed as a table.