Additional file 5 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Table S5. Verbs and nouns used to construct Interaction Type sub-ontology

Additional file 11 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Table S11. The sentences with PPIs and detection method information extracted from “BioCreAtIvE PPI” corpus by the PPIO-based method

Additional file 13 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Supplementary Material and Methods

Additional file 4 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Table S4. The sentences with PPIs and PPI annotations mined manually from “BioCreAtIvE PPI” corpus

Table_1_Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients.docx

BackgroundSurgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection.MethodsPatients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots.Results118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves.ConclusionThe nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment.</p

Additional file 12 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Table S12. The performance of dictionary-based method on test dataset

Additional file 10 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Table S10. The sentences with PPIs and proteins’ role & state information extracted from “BioCreAtIvE PPI” corpus by the PPIO-based method

Additional file 3 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Table S3.The sentences with protein-protein interactions in “BioCreAtIvE PPI” corpus

Additional file 7 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Table S7. The sentences with PPIs and subcellular location information extracted from “BioCreAtIvE PPI” corpus using the PPIO-based method

Additional file 8 of The protein-protein interaction ontology: for better representing and capturing the biological context of protein interaction

Table S8. The sentences with PPIs and biological function information extracted from “BioCreAtIvE PPI” corpus by the PPIO-based method