774 Nucleic Acids Research, 2009, Vol. 37, No. 3 Table 3. Ontology occurrence count in sentences from the test corpus, separated by diabetes and cancer phenotypes Gene and protein perturbations Total increasing modiﬁcations General increasing modiﬁcations Activation Administered, -ion Dose, -age, -dependent Ectopic Enhanced . . . expression Exogenous i.c.v. i.p. Increased Induction Infused, -ion Injected, -ion Intracerebroventricular Intraperitoneal mg/kg,/kg Oral Overexpressed, -ration Peripherally Recombinant Restoration Subcutaneous Systemic Treatment Up-regulation DNA increasing modiﬁcations Adenoviral Adenovirus Gain-of-function Gene delivery Transgenic mRNA increasing modiﬁcations Inducible Protein increasing modiﬁcations Activator Agonist Analog/analogue Cancer 266 66 7 11 7 10 6 0 0 26 8 0 7 0 1 0 6 38 0 7 2 0 1 24 8 2 0 2 1 3 2 10 9 2 Diabetes 586 12 120 86 0 0 23 11 7 18 2 35 66 51 16 8 4 0 7 10 0 2 9 22 0 1 0 0 3 2 0 0 54 7 Total decreasing modiﬁcations General decreasing modiﬁcations Down-regulated, -ion Inactivation Inhibition Repression Suppression DNA decreasing modiﬁcations Deﬁciency, -ent Deletion Dominant-negative Knockout Loss Mutant Mutated, -ion mRNA decreasing modiﬁcations Anti(-)sense Interference Interfering RNA Knockdown RNA interference RNAi Short hairpin RNA Silenced siRNA Protein decreasing modiﬁcations Antagonist Anti- Antibody, -ies Blockade, -ing Deactivation Decoy Fc- Inhibitor Inverse agonist mAbs Neutralization Reduced . . . activity Soluble Targeting Cancer 267 Diabetes 110 12 5 30 1 10 15 3 3 1 12 0 0 9 5 2 12 4 2 1 1 15 7 25 11 12 1 2 0 53 0 1 1 1 4 6 0 0 7 0 3 12 2 0 2 13 0 3 3 1 0 0 0 0 0 0 0 28 4 3 8 0 0 0 15 1 0 0 5 0 0 Note that total increased and decreased perturbations exceeds values in Table 2 since multiple perturbation terms may appear in a single sentence. perturbation–detection algorithm built using diﬀerent combinations of feature sets. Our algorithm detected per- turbations with F-measure of 79.4%. Detection of increased and decreased perturbations had a lower F-measure, 72.9% and 71.2%, respectively. When we excluded the perturbation ontology in feature generation, results were only slightly lower, whereas when we exclu- sively used the perturbation ontology, results were much lower, notably due to reduced recall. ‘Straightforward’ relationships (91.6% of the total) were those that evalua- tors annotated without consultation with other annota- tors. These relationships were less challenging for humans, and the algorithm had better performance over this subset than overall. Due to absence of previous work in perturbation detection, these results cannot be com- pared, but they fall in ranges typical of other successful biomedical text mining tools. To determine whether the disease impacted the fre- quency of perturbation types, we compared cancer and diabetes literature. The diabetes literature was signiﬁ- cantly enriched in increased perturbations, whereas the cancer literature showed an even distribution between increased and decreased perturbations. In cancer, more perturbations were performed with antisense oligonucleo- tide (n = 9), antibody (n = 16) and RNA interference (n = 26) than in diabetes (antisense, n = 3; antibody, n = 2; RNAi, n = 0). Perturbations in diabetes were more frequently described as injections (n = 132) and/or administered by dose (n = 57) than in cancer (dose, n = 3; injection, n = 6). Perturbations in cancer were also more frequently described as being in vivo (n = 12) or in vitro (n = 14) than in diabetes (in vivo, n = 5; in vitro, n = 0). We examined the sentences for frequently occurring terms, grouped by their level of regulation (i.e. protein, mRNA or DNA), if known. Table 3 illustrates the wide variation in terms and aﬃxes used in both diseases. Many of the terms related to dosing and routes of administration (e.g. administration, intracerebroventricular, injection) show a strong dominance in the diabetes literature compared to the cancer literature. Although these usually indicate an increasing perturbation, there can be exceptions, such as systemic delivery of an inhibitor.