Time-stage driven pathfinding framework for optimized medical treatments

References

• Amazon Web Services. Amazon s3; 2023a. Available from: https://aws.amazon.com/s3/.
   Google Scholar
• Amazon Web Services. Aws lambda; 2023b. Available from: https://aws.amazon.com/lambda/.
   Google Scholar
• Aslanyan, L., Gishyan, K., Sahakyan, H. Target class classification recursion preliminaries. In: 13th Conference for Data Analysis Methods for Software Systems; December; 2022. p. 6–17. Available from: https://www.mii.lt/damss/files/damss_2022.pdf.
   Google Scholar
• Bhimavarapu, U., Chintalapudi, N., & Battineni, G. (2022). A fair and safe usage drug recommendation system in medical emergencies by a stacked ann. Algorithms, 15(6), 186. https://doi.org/10.3390/a15060186
   Web of Science ®Google Scholar
• Bhoi, S., Lee, M. L., Hsu, W., Fang, H. S. A., & Tan, N. C. (2021). Personalizing medication recommendation with a graph-based approach. ACM Transactions on Information Systems (TOIS), 40(3), 1–23. https://doi.org/10.1145/3488668
   Web of Science ®Google Scholar
• Calzolari, D., Bruschi, S., Coquin, L., Schofield, J., Feala, J. D., Reed, J. C., McCulloch, A. D., & Paternostro, G. (2008). Search algorithms as a framework for the optimization of drug combinations. PLoS Computational Biology, 4(12), e1000249. https://doi.org/10.1371/journal.pcbi.1000249
   PubMed Web of Science ®Google Scholar
• Canny, J., & Reif, J. (1987). New lower bound techniques for robot motion planning problems. Proceedings of the 28th Annual Symposium on Foundations of Computer Science (sfcs 1987), Los Angeles, CA (pp. 49–60). http://ieee-focs.org/
   Google Scholar
• Cheng, F., Desai, R. J., Handy, D. E., Wang, R., Schneeweiss, S., Barabási, A.-L., & Loscalzo, J. (2018). Network-based approach to prediction and population-based validation of in silico drug repurposing. Nature Communications, 9(1), 2691. https://doi.org/10.1038/s41467-018-05116-5
   PubMed Web of Science ®Google Scholar
• Cheng, F., Kovács, I. A., & Barabási, A. L. (2019). Publisher Correction: Network-based prediction of drug combinations. Nature Communications, 10(1), 1–11. https://doi.org/10.1038/s41467-019-09692-y
   PubMedGoogle Scholar
• Chen, L., Hao Xing, Z., Huang, T., Shu, Y., Huang, G., & Li, H.-P. (2016). Application of the shortest path algorithm for the discovery of breast cancer-related genes. Current Bioinformatics, 11(1), 51–58. https://doi.org/10.2174/1574893611666151119220024
   Web of Science ®Google Scholar
• David Poole, A. M., Mackworth, A. K. (2017). Artificial intelligence: Foundations of computational agents, 2e. Cambridge University Press. https://doi.org/10.1017/9781108164085
   Google Scholar
• Django Software Foundation. Django; (2022). Available from: https://djangoproject.com.
   Google Scholar
• Gribova, V., Moskalenko, P., Petryaeva, M., & Okun, D. (2019). Cloud environment for development and use of software systems for clinical medicine and education. Proceedings of the 7th Scientific Conference on Information Technologies for Intelligent Decision Making Support (ITIDS 2019), Ufa, Russia (pp. 225–229). Atlantis Press. https://www.atlantis-press.com/proceedings/itids-19#:~:text=Welcome%20to%20the%207th%20Scientific,%2C%202019%20in%20Ufa%2C%20Russia
   Google Scholar
• Guney, E., Menche, J., Vidal, M., & Barábasi, A.-L. (2016). Network-based in silico drug efficacy screening. Nature Communications, 7(1), 10331. https://doi.org/10.1038/ncomms10331
   PubMedGoogle Scholar
• Hagberg, A., Swart, P. S., & Chult, D. (2008). Exploring network structure, dynamics, and function using networkx. Los Alamos National lab.(lanl). NM (United States).
   Google Scholar
• Hopkins, A. L. (2008). Network pharmacology: The next paradigm in drug discovery. Nature Chemical Biology, 4(11), 682–690. https://doi.org/10.1038/nchembio.118
   PubMed Web of Science ®Google Scholar
• Jiang, M., Chen, Y., Zhang, Y., Chen, L., Zhang, N., Huang, T., Cai, Y.-D., & Kong, X. (2013). Identification of hepatocellular carcinoma related genes with k-th shortest paths in a protein–protein interaction network. Molecular BioSystems, 9(11), 2720–2728. https://doi.org/10.1039/c3mb70089e
   PubMedGoogle Scholar
• Jiang, Y., Shu, Y., Shi, Y., Li, L.-P., Yuan, F., & Ren, H. (2014). Identifying gastric cancer related genes using the shortest path algorithm and protein-protein interaction network. BioMed Research International 2014, 2014, 1–9. https://doi.org/10.1155/2014/371397
   Web of Science ®Google Scholar
• Jiang, Y., Zhang, P., Li, L. P., He, Y.-C., Gao, R.-J., & Gao, Y.-F. (2015). Identification of novel thyroid cancer-related genes and chemicals using shortest path algorithm. BioMed Research International 2015, 2015, 1–8. https://doi.org/10.1155/2015/964795
   Web of Science ®Google Scholar
• Johnson, A. E., Pollard, T. J., Shen, L., Lehman, L. W. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., & Mark, R. G. (2016). Mimic-iii, a freely accessible critical care database. Scientific Data, 3(1), 1–9. https://doi.org/10.1038/sdata.2016.35
   Google Scholar
• Konakalla, S. V. (2014). PATH FINDING – Dijkstra’s algorithm. https://cs.indstate.edu/rjaliparthive/dijkstras.pdf
   Google Scholar
• Le, H., Tran, T., & Venkatesh, S. (2018). Dual memory neural computer for asynchronous two-view sequential learning. Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining, London, United Kingdom (pp. 1637–1645). https://dl.acm.org/doi/proceedings/10.1145/3219819
   Google Scholar
• Li, B. Q., You, J., Chen, L., Zhang, J., Zhang, N., Li, H.-P., Huang, T., Kong, X.-Y., & Cai, Y.-D. (2013). Identification of lung-cancer-related genes with the shortest path approach in a protein-protein interaction network. BioMed Research International 2013, 2013, 1–8. https://doi.org/10.1155/2013/267375
   Web of Science ®Google Scholar
• Malas, T. B., Vlietstra, W. J., Kudrin, R., Starikov, S., Charrout, M., Roos, M., Peters, D. J. M., Kors, J. A., Vos, R., ‘t Hoen, P. A. C., van Mulligen, E. M., & Hettne, K. M. (2019). Drug prioritization using the semantic properties of a knowledge graph. Scientific Reports, 9(1), 1–10. https://doi.org/10.1038/s41598-019-42806-6
   PubMedGoogle Scholar
• Mao, C., Yao, L., & Luo, Y. (2022). Medgcn: Medication recommendation and lab test imputation via graph convolutional networks. Journal of Biomedical Informatics, 127, 104000. https://doi.org/10.1016/j.jbi.2022.104000
   PubMed Web of Science ®Google Scholar
• Menche, J., Sharma, A., Kitsak, M., Ghiassian, S. D., Vidal, M., Loscalzo, J., & Barabasi, A.-L. (2015). Uncovering disease-disease relationships through the incomplete interactome. Science, 347(6224), 1257601. https://doi.org/10.1126/science.1257601
   PubMed Web of Science ®Google Scholar
• Rajan, S. P., & Paranthaman, M. (2022). Artificial Intelligence in Healthcare: Algorithms and Decision Support Systems. In C. Venkatesh, N. Rengarajan, P. Ponmurugan, & S. Balamurugan (Eds.), Smart Systems for Industrial Applications (pp. 173–197). https://doi.org/10.1002/9781119762010.ch7
   Google Scholar
• Raveh, B., Enosh, A., & Halperin, D. (2011). A little more, a lot better: Improving path quality by a path-merging algorithm. IEEE Transactions on Robotics, 27(2), 365–371. https://doi.org/10.1109/TRO.2010.2098622
   Web of Science ®Google Scholar
• Rivas-Barragan, D., Mubeen, S., Guim Bernat, F., Hofmann-Apitius, M., & Domingo-Fernández, D. (2020). Drug2ways: Reasoning over causal paths in biological networks for drug discovery. PLoS Computational Biology, 16(12), e1008464. https://doi.org/10.1371/journal.pcbi.1008464
   PubMed Web of Science ®Google Scholar
• Shang, J., Ma, T., & Xiao, C. (2019). Pre-training of graph augmented transformers for medication recommendation. arXiv Preprint arXiv: 190600346.
   Google Scholar
• Shang, J., Xiao, C., Ma, T., Li, H., & Sun, J. (2019). Gamenet: Graph augmented memory networks for recommending medication combination. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA (Vol. 33, No. (01), pp. 1126–1133). https://ojs.aaai.org/index.php/AAAI/issue/view/246
   Google Scholar
• Vidal, M., Cusick, M. E., & Barabási, A. L. (2011). Interactome networks and human disease. Cell, 144(6), 986–998. https://doi.org/10.1016/j.cell.2011.02.016
   PubMed Web of Science ®Google Scholar
• Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R. … Halchenko, Y. O. (2020). SciPy 1.0: Fundamental algorithms for Scientific Computing in Python. Nature Methods, 17(3), 261–272. https://doi.org/10.1038/s41592-019-0686-2
   PubMed Web of Science ®Google Scholar
• Yıldırım, M. A., Goh, K. I., Cusick, M. E., Barabási, A.-L., & Vidal, M. (2007). Drug—target network. Nature Biotechnology, 25(10), 1119–1126. https://doi.org/10.1038/nbt1338
   PubMed Web of Science ®Google Scholar
• Zhang, Y., Chen, R., Tang, J., Stewart, W. F., & Sun, J. (2017). LEAP: learning to prescribe effective and safe treatment combinations for multimorbidity. Proceedings of the 23rd ACM SIGKDD international conference on knowledge Discovery and data Mining, Halifax NS, Canada (pp. 1315–1324). https://dl.acm.org/doi/proceedings/10.1145/3097983
   Google Scholar
• Zhang, J., Jiang, M., Yuan, F., Feng, K.-Y., Cai, Y.-D., Xu, X., & Chen, L. (2013). Identification of age-related macular degeneration related genes by applying shortest path algorithm in protein-protein interaction network. BioMed Research International 2013, 2013, 1–8. https://doi.org/10.1155/2013/523415
   Web of Science ®Google Scholar