參考文獻 |
1. Ahlqvist, E., T.S. Ahluwalia, and L. Groop, Genetics of type 2 diabetes. Clinical chemistry, 2011. 57(2): p. 241-254.
2. Kunte, H., et al., Sulfonylureas improve outcome in patients with type 2 diabetes and acute ischemic stroke. Stroke, 2007. 38(9): p. 2526-2530.
3. Chillaron, J., et al., Insulin resistance and hypertension in patients with type 1 diabetes. Journal of diabetes and its complications, 2011. 25(4): p. 232.
4. Chawla, A., R. Chawla, and S. Jaggi, Microvasular and macrovascular complications in diabetes mellitus: distinct or continuum? Indian journal of endocrinology and metabolism, 2016. 20(4): p. 546.
5. Leon, B.M. and T.M. Maddox, Diabetes and cardiovascular disease: Epidemiology, biological mechanisms, treatment recommendations and future research. World journal of diabetes, 2015. 6(13): p. 1246.
6. Zhou, H., X. Zhang, and J. Lu, Progress on diabetic cerebrovascular diseases. Bosnian journal of basic medical sciences, 2014. 14(4): p. 185.
7. Martin, E.T., et al., Diabetes and risk of surgical site infection: a systematic review and meta-analysis. infection control & hospital epidemiology, 2016. 37(1): p. 88-99.
8. Bunescu, R., et al., Comparative experiments on learning information extractors for proteins and their interactions. Artificial intelligence in medicine, 2005. 33(2): p. 139-155.
9. Pyysalo, S., et al., BioInfer: a corpus for information extraction in the biomedical domain. BMC bioinformatics, 2007. 8(1): p. 50.
10. Wishart, D.S., et al., DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic acids research, 2017. 46(D1): p. D1074-D1082.
11. Pyysalo, S., T. Ohta, and S. Ananiadou. Overview of the cancer genetics (CG) task of BioNLP Shared Task 2013. in Proceedings of the BioNLP Shared Task 2013 Workshop. 2013.
12. Li, J., et al., BioCreative V CDR task corpus: a resource for chemical disease relation extraction. Database, 2016.
13. Peng, Y., C.-H. Wei, and Z. Lu, Improving chemical disease relation extraction with rich features and weakly labeled data. Journal of cheminformatics, 2016. 8(1): p. 53.
14. Pons, E., et al., Extraction of chemical-induced diseases using prior knowledge and textual information. Database, 2016: p. baw046.
15. Xu, J., et al., CD-REST: a system for extracting chemical-induced disease relation in literature. Database, 2016.
16. Asada, M., M. Miwa, and Y. Sasaki, Extracting Drug-Drug Interactions with Attention CNNs. BioNLP, 2017: p. 9-18.
17. Peng, Y. and Z. Lu, Deep learning for extracting protein-protein interactions from biomedical literature. BioNLP, 2017: p. 29-38.
18. Zhao, Z., et al., Drug drug interaction extraction from biomedical literature using syntax convolutional neural network. Bioinformatics, 2016. 32(22): p. 3444-3453.
19. Peng, Y., et al., Chemical-protein relation extraction with ensembles of SVM, CNN, and RNN models. arXiv preprint arXiv:1802.01255, 2018.
20. Feng, Z., Z. Sun, and L. Jin. Learning deep neural network using max-margin minimum classification error. in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on. 2016. IEEE.
21. Tang, Y., Deep learning using linear support vector machines. arXiv preprint arXiv:1306.0239, 2013.
22. Lee, J., et al. On the efficacy of per-relation basis performance evaluation for PPI extraction and a high-precision rule-based approach. in BMC medical informatics and decision making. 2013. BioMed Central.
23. Nguyen, N.T., et al., Wide-coverage relation extraction from MEDLINE using deep syntax. BMC bioinformatics, 2015. 16(1): p. 107.
24. Lipscomb, C.E., Medical subject headings (MeSH). Bulletin of the Medical Library Association, 2000. 88(3): p. 265.
25. Davis, A.P., et al., The comparative toxicogenomics database: update 2017. Nucleic acids research, 2016. 45(D1): p. D972-D978.
26. Leaman, R., R. Islamaj Do?an, and Z. Lu, DNorm: disease name normalization with pairwise learning to rank. Bioinformatics, 2013. 29(22): p. 2909-2917.
27. Mikolov, T., et al. Distributed representations of words and phrases and their compositionality. in Advances in neural information processing systems. 2013.
28. Mikolov, T., et al., Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.
29. Mikolov, T., W.-t. Yih, and G. Zweig. Linguistic regularities in continuous space word representations. in Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2013.
30. Mikolov, T., et al. Recurrent neural network based language model. in Eleventh Annual Conference of the International Speech Communication Association. 2010.
31. SPFGH, M. and T.S.S. Ananiadou, Distributional semantics resources for biomedical text processing. 2013.
32. Peng, Y. and Z. Lu, Deep learning for extracting protein-protein interactions from biomedical literature. arXiv preprint arXiv:1706.01556, 2017.
33. Chang, C.-C. and C.-J. Lin, LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2011. 2(3): p. 27.
34. Xu, M., et al., Deep Learning for Person Reidentification Using Support Vector Machines. Advances in Multimedia, 2017. |