An open-source framework for large-scale, flexible evaluation of biomedical text mining systems-1

Mbined gene tagger performance. GN system performance based on each of the three methods for combining gene tagger output (Overlapping, Consensus, Consensus followed by Overlapping). GN System performance highlighting the combination of the overlapping filter with and without use of the dictionary-based GM system. Data points generated using the other filters are shown in gray. Same as C, with the presence/absence of another representative tagger shown. and : GN system performance as it relates to combined gene tagger precision and recall, respectively.<p><b>Copyright information:</b></p><p>Taken from "An open-source framework for large-scale, flexible evaluation of biomedical text mining systems"</p><p>http://www.j-biomed-discovery.com/content/3/1/1</p><p>Journal of Biomedical Discovery and Collaboration 2008;3():1-1.</p><p>Published online 29 Jan 2008</p><p>PMCID:PMC2276192.</p><p></p

An open-source framework for large-scale, flexible evaluation of biomedical text mining systems-2

Ncology, left; Bio1, right). There are 45 data points in each graph. Five evaluation metrics – , Strict: spans must match exactly; , Sloppy: spans must overlap; , LeftMatch: span starts must match; , RightMatch: span ends must match; , EitherMatch: span start or end must match – were used to evaluate each tagger. Different colors are used to distinguish between the taggers. F-measure contour lines are displayed in gray, with the corresponding value listed on the right, also in gray.<p><b>Copyright information:</b></p><p>Taken from "An open-source framework for large-scale, flexible evaluation of biomedical text mining systems"</p><p>http://www.j-biomed-discovery.com/content/3/1/1</p><p>Journal of Biomedical Discovery and Collaboration 2008;3():1-1.</p><p>Published online 29 Jan 2008</p><p>PMCID:PMC2276192.</p><p></p

Improving protein function prediction methods with integrated literature data-3

3 in the MIPS functional hierarchy. a) Majority, b) Functional Flow. Abbreviations: PPI ONLY – only edges from experiments measuring protein-protein interactions; PPI+COLIT – PPI edges combined with edges between proteins mentioned at least twice together in literature abstracts.<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p

Improving protein function prediction methods with integrated literature data-2

Ted correctly (TP), for FP up to 100. Abbreviations as in Figure 2.<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p

Improving protein function prediction methods with integrated literature data-4

Ine the co-occurrence interaction set. Shown is the number of true positives (TP) when the scoring threshold is set to yield 100 false positives (FP) (y axis). The values of the x-axis denote instances of Functional Flow on graphs combining PPI and the interaction sets for each corresponding setting of the co-occurrence threshold (x = -1 shows PPI ONLY and x = 0–9 denote PPI plus the datasets obtained using thresholds 0.0 to 0.9). The lines are annotated to denote the MUT, HYG and ACF metrics. The best and worst performers respectively, over all co-occurrence measure and all thresholds, are shown in parentheses below the plot title. These combinations appear as Best and Worst in Figures 2 and 3.<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p

Improving protein function prediction methods with integrated literature data-1

Ted correctly (TP). Abbreviations: GOMF – GO SLIM Molecular Function; GOBP – GO SLIM Biological Process; PPI ONLY – only edges from experiments measuring protein-protein interactions, such as yeast two-hybrid and affinity precipitation; GENETIC ONLY – only edges from genetic assays, such as synthetic lethality studies; PPI+GENETIC – edges from both PPI and from genetic assays, such as synthetic lethality studies; PPI+COLIT – edges from both PPI and edges between proteins found by literature co-occurrence, where Best and Worst correspond to the best and worst combinations of threshold setting and co-occurrence measure, respectively (. Figure 5).<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p

Improving protein function prediction methods with integrated literature data-0

O-occurrence measures. Abbreviations: MUT – Mutual Information Measure; HYG – Hypergeometric Measure; ACF – Asymmetric Co-occurrence Fraction.<p><b>Copyright information:</b></p><p>Taken from "Improving protein function prediction methods with integrated literature data"</p><p>http://www.biomedcentral.com/1471-2105/9/198</p><p>BMC Bioinformatics 2008;9():198-198.</p><p>Published online 15 Apr 2008</p><p>PMCID:PMC2375131.</p><p></p