Predicting Encounters with Artificial Intelligence
Abstract
Cervical cancer represents a widespread global health concern, responsible for considerable morbidity and mortality among women around the world. The illness is mainly triggered by long-lasting infection with high-risk human papillomavirus (HPV) types. Although effective preventive measures such as HPV vaccination and screening are available, cervical cancer persists as a primary cause of cancer-related fatalities among women in low- and middleincome nations. Recent progress in understanding the biology of cervical cancer has prompted the creation of new therapeutic methods, encompassing targeted therapies and immunotherapies. These cutting-edge treatments have displayed encouraging outcomes in enhancing patient results, especially in advanced-stage and recurrent cases. This review intends to deliver a thorough summary of the existing knowledge regarding cervical cancer, encompassing its epidemiology, prevention methods, diagnostic techniques, and treatment possibilities. We also examine recent advancements in cervical cancer research and their potential effects on patient care.
Keywords:
Drug, Drug–Drug Interaction, Database, Web Server, Computational Model
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References
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https://doi.org/10.1093/bib/bbac427
4. Ehsani R, Drabløs F. Robust distance measures for k NN classification of cancer data. Cancer informatics. 2020 Oct;19:1176935120965542. 1176935120965542. https://doi.org/10.1177/1176935120965542
5. Asikainen AH, Ruuskanen J, Tuppurainen KA. Performance of (consensus) kNN QSAR for predicting estrogenic activity in a large diverse set of organic compounds. SAR and QSAR in Environmental Research. 2004 Feb 1;15(1):19-32.
https://doi.org/10.1080/1062936032000169642
6. Lee JY, Styczynski MP. NSkNN: a modified k-nearest neighbors approach for imputing metabolomics data. Metabolomics. 2018 Dec;14:1-2.
https://doi.org/10.1007/s11306-018-1451-8
7. Borenstein E. Computational systems biology and in silico modeling of the human microbiome. Briefings in bioinformatics. 2012 Nov 1;13(6):769-80.
https://doi.org/10.1093/bib/bbs022
8. Bielza, C., & Larranaga, P. (2014). Discrete Bayesian network classifiers: A survey. ACM Computing Surveys (CSUR), 47(1), 1-43.
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9. Sun H. A naive Bayes classifier for prediction of multidrug resistance reversal activity on the basis of atom typing. Journal of Medicinal Chemistry. 2005 Jun 16;48(12):4031-9.
https://doi.org/10.1021/jm050180t
10. Pang, X., Fu, W., Wang, J., Kang, D., Xu, L., Zhao, Y., ... & Du, G. H. (2018). Identification of estrogen receptor α antagonists from natural products via in vitro and in silico approaches. Oxidative medicine and cellular longevity, 2018(1), 6040149.
https://doi.org/10.1155/2018/6040149
11. Huang MW, Chen CW, Lin WC, Ke SW, Tsai CF. SVM
and SVM ensembles in breast cancer prediction. PloS one. 2017 Jan 6;12(1):e0161501.
https://doi.org/10.1371/journal.pone.0161501
12. Sheridan RP, Wang WM, Liaw A, Ma J, Gifford EM.
Extreme gradient boosting as a method for
quantitative structure–activity relationships. Journal of chemical information and modeling. 2016 Dec 27;56(12):2353-60.
https://doi.org/10.1021/acs.jcim.6b00591
13. Stoltzfus JC. Logistic regression: a brief primer. Academic emergency medicine. 2011 Oct;18(10):1099-104.
https://doi.org/10.1111/j.1553-2712.2011.01185.x
14. Xuan P, Sun C, Zhang T, Ye Y, Shen T, Dong Y. Gradient boosting decision tree-based method for predicting interactions between target genes and drugs. Frontiers in genetics. 2019 May 31;10:459.
https://doi.org/10.3389/fgene.2019.00459
15. Li T, Yang K, Stein JD, Nallasamy N. Gradient boosting decision tree algorithm for the prediction of postoperative intraocular lens position in cataract surgery. Translational Vision Science & Technology. 2020 Dec 1;9(13):38-.
https://doi.org/10.1167/tvst.9.13.38
16. Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic acids research. 2006 Jan 1;34(suppl_1):D668-72.
https://doi.org/10.1093/nar/gkj067
17. Wang Y, Sohn S, Liu S, Shen F, Wang L, Atkinson EJ, Amin S, Liu H. A clinical text classification paradigm using weak supervision and deep representation. BMC medical informatics and decision making. 2019 Dec;19:1-3.
https://doi.org/10.3390/make5020036
18. Kumar Shukla P, Kumar Shukla P, Sharma P, Rawat P, Samar J, Moriwal R, Kaur M. Efficient prediction of drug–drug interaction using deep learning models. IET Systems Biology. 2020 Aug;14(4):211-6.
https://doi.org/10.1049/iet-syb.2019.0116
19. Sejnowski TJ. The unreasonable effectiveness of deep learning in artificial intelligence. Proceedings of the National Academy of Sciences. 2020 Dec 1;117(48):30033-8.
https://doi.org/10.1073/pnas.1907373117
20. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, Cui C, Corrado G, Thrun S, Dean J. A guide to deep learning in healthcare. Nature medicine. 2019 Jan;25(1):24-9.
https://doi.org/10.1038/s41591-018-0316-z
21. Hou WJ, Ceesay B. Extraction of drug–drug interaction using neural embedding. Journal of bioinformatics and computational biology. 2018 Dec 19;16(06):1840027.
https://doi.org/10.1142/S0219720018400279
22. Hwisa NT, Gindi S, Rao CB, Katakam P, Rao Chandu B. Evaluation of Antiulcer Activity of Picrasma Quassioides Bennett Aqueous Extract in Rodents.
Vedic Res. Int. Phytomedicine. 2013;1:27.
https://www.researchgate.net/publication/248995990
Published
03/08/2025
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[1]
chaitanya K. Rekala, “Predicting Encounters with Artificial Intelligence”, Int J Indig Herb Drug, vol. 10, no. 3, pp. 7-12, Aug. 2025.
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