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report.tex
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report.tex
@@ -125,8 +125,8 @@
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% КОНЕЦ ТИТУЛЬНОГО ЛИСТА
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\newpage
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% \tableofcontents
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% \newpage
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\tableofcontents
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\newpage
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\section*{Keywords}
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\addcontentsline{toc}{section}{Keywords}
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@@ -272,6 +272,63 @@
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Overall, the application of machine learning in assessing drug resistance represents a novel approach in cancer treatment, offering lots of opportunities to enhance the precision and effectiveness of therapies. By continuing to advance machine learning algorithms and support their integration into clinical practice, the medical community can significantly improve the management of drug-resistant cancers, ultimately reducing mortality rates and improving the quality of life for patients worldwide.
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\newpage
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\vspace{-1.5cm}
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\begin{thebibliography}{0}
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\bibitem{cancer}
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International Agency for Research on Cancer, F. Bray, IARC, E. Weiderpass, and World Health Organization, “Latest global cancer data: Cancer burden rises to 19.3 million new cases and 10.0 million cancer deaths in 2020,” IARC, Dec. 15, 2020. \url{https://www.iarc.who.int/wp-content/uploads/2020/12/pr292_E.pdf} (accessed Dec. 01, 2024).
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\bibitem{therapy}
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Sh. Huang and B. O. Sullivan, “Oral cancer: Current role of radiotherapy and chemotherapy,” Medicina Oral, Patología Oral Y Cirugía Bucal, pp. e233–e240, Jan. 2013, doi: 10.4317/medoral.18772.
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\bibitem{treateoc}
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L. Kuroki and S. R. Guntupalli, “Treatment of epithelial ovarian cancer,” BMJ, p. m3773, Nov. 2020, doi: 10.1136/bmj.m3773.
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\bibitem{resistance}
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S. W. Johnson, R. F. Ozols, and T. C. Hamilton, “Mechanisms of drug resistance in ovarian cancer,” Cancer, vol. 71, no. S2, pp. 644–649, Aug. 2010, doi: 10.1002/cncr.2820710224.
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\bibitem{paclitaxel}
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L. Xin et al., “Classification of Paclitaxel-resistant Ovarian Cancer Cells Using Holographic Flow Cytometry through Interpretable Machine Learning,” Sensors and Actuators B Chemical, vol. 414, p. 135948, May 2024, doi: 10.1016/j.snb.2024.135948.
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\bibitem{cervical}
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L. Guo, W. Wang, X. Xie, S. Wang, and Y. Zhang, “Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer,” Biomedicine \& Pharmacotherapy, vol. 159, p. 114256, Jan. 2023, doi: 10.1016/j.biopha.2023.114256.
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\bibitem{mlrole}
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Y. Jiang, M. Yang, S. Wang, X. Li, and Y. Sun, “Emerging role of deep learning‐based artificial intelligence in tumor pathology,” Cancer Communications, vol. 40, no. 4, pp. 154–166, Apr. 2020, doi: 10.1002/cac2.12012.
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\bibitem{shap}
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S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” arXiv (Cornell University), Jan. 2017, doi: 10.48550/arxiv.1705.07874.
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\bibitem{lasso}
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R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society Series B (Statistical Methodology), vol. 58, no. 1, pp. 267–288, Jan. 1996, doi: 10.1111/j.2517-6161.1996.tb02080.x.
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\bibitem{dalex}
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P. Biecek, “DALEX: Explainers for Complex Predictive Models in R,” Zenodo (CERN European Organization for Nuclear Research), Feb. 2020, doi: 10.5281/zenodo.3670940.
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\bibitem{mitochondria}
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Z. Liu et al., “Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer,” Translational Oncology, vol. 42, p. 101896, Feb. 2024, doi: 10.1016/j.tranon.2024.101896.
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\bibitem{sers}
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J. Zhang et al., “Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction,” Journal of Photochemistry and Photobiology B Biology, vol. 257, p. 112968, Jun. 2024, doi: 10.1016/j.jphotobiol.2024.112968.
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\bibitem{heterogeneity}
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Q. Zhu et al., “Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer,” Translational Oncology, vol. 40, p. 101855, Jan. 2024, doi: 10.1016/j.tranon.2023.101855.
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\bibitem{cellprofile}
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C. McQuin et al., “CellProfiler 3.0: Next-generation image processing for biology,” PLoS Biology, vol. 16, no. 7, p. e2005970, Jul. 2018, doi: 10.1371/journal.pbio.2005970.
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\bibitem{platinum}
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S. Shen et al., “A Predictive Model for Initial Platinum‐Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue‐Derived Small Extracellular Vesicles,” Journal of Extracellular Vesicles, vol. 13, no. 8, Aug. 2024, doi: 10.1002/jev2.12486.
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\bibitem{kras}
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X. Lin et al., “Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods,” Biochimica Et Biophysica Acta (BBA) - General Subjects, vol. 1867, no. 12, p. 130484, Oct. 2023, doi: 10.1016/j.bbagen.2023.130484.
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\bibitem{glut}
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S.-Y. Lu et al., “Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance,” Heliyon, vol. 10, no. 17, p. e36684, Aug. 2024, doi: 10.1016/j.heliyon.2024.e36684.
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\bibitem{tabular}
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A. Nasimian, M. Ahmed, I. Hedenfalk, and J. U. Kazi, “A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer,” Computational and Structural Biotechnology Journal, vol. 21, pp. 956–964, doi: 10.1016/j.csbj.2023.01.020.
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\bibitem{deep}
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J. Longden et al., “Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space,” Cell Reports, vol. 34, no. 3, p. 108657, Jan. 2021, doi: 10.1016/j.celrep.2020.108657.
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\bibitem{PerkinElmer}
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“PerkinElmer | Science with purpose.” \url{https://content.perkinelmer.com/} (accessed Dec. 01, 2024).
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\bibitem{geo}
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“Gene Expression Omnibus (GEO) Database.” \url{https://www.ncbi.nlm.nih.gov/geo/} (accessed Dec. 01, 2024).
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\bibitem{tcga}
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“The Cancer Genome Atlas Program (TCGA),” Cancer.gov. \url{https://www.cancer.gov/ccg/research/genome-sequencing/tcga} (accessed Dec. 01, 2024).
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\bibitem{atcc}
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“ATCC: The Global Bioresource Center,” ATCC. \url{https://www.atcc.org/} (accessed Dec. 01, 2024).
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\bibitem{ega}
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“EGA European Genome-Phenome Archive,” The European Bioinformatics Institute (EMBL-EBI). \url{https://ega-archive.org/} (accessed Dec. 01, 2024).
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\bibitem{r-lang}
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“R: The R Project for Statistical Computing.” \url{https://www.r-project.org/} (accessed Dec. 01, 2024).
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\end{thebibliography}
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\addtocounter{table}{1}
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\includepdf[pages={1}, fitpaper, pagecommand={
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\thispagestyle{empty}
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@@ -296,60 +353,4 @@
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};
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\end{tikzpicture}
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}]{results_table/results.pdf}
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\newpage
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\vspace{-1.5cm}
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\begin{thebibliography}{0}
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\bibitem{paclitaxel}
|
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L. Xin et al., “Classification of Paclitaxel-resistant Ovarian Cancer Cells Using Holographic Flow Cytometry through Interpretable Machine Learning,” Sensors and Actuators B Chemical, vol. 414, p. 135948, May 2024, doi: 10.1016/j.snb.2024.135948.
|
||||
\bibitem{heterogeneity}
|
||||
Q. Zhu et al., “Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer,” Translational Oncology, vol. 40, p. 101855, Jan. 2024, doi: 10.1016/j.tranon.2023.101855.
|
||||
\bibitem{mitochondria}
|
||||
Z. Liu et al., “Mitochondria-related chemoradiotherapy resistance genes-based machine learning model associated with immune cell infiltration on the prognosis of esophageal cancer and its value in pan-cancer,” Translational Oncology, vol. 42, p. 101896, Feb. 2024, doi: 10.1016/j.tranon.2024.101896.
|
||||
\bibitem{sers}
|
||||
J. Zhang et al., “Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction,” Journal of Photochemistry and Photobiology B Biology, vol. 257, p. 112968, Jun. 2024, doi: 10.1016/j.jphotobiol.2024.112968.
|
||||
\bibitem{platinum}
|
||||
S. Shen et al., “A Predictive Model for Initial Platinum‐Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue‐Derived Small Extracellular Vesicles,” Journal of Extracellular Vesicles, vol. 13, no. 8, Aug. 2024, doi: 10.1002/jev2.12486.
|
||||
\bibitem{kras}
|
||||
X. Lin et al., “Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods,” Biochimica Et Biophysica Acta (BBA) - General Subjects, vol. 1867, no. 12, p. 130484, Oct. 2023, doi: 10.1016/j.bbagen.2023.130484.
|
||||
\bibitem{glut}
|
||||
S.-Y. Lu et al., “Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance,” Heliyon, vol. 10, no. 17, p. e36684, Aug. 2024, doi: 10.1016/j.heliyon.2024.e36684.
|
||||
\bibitem{cervical}
|
||||
L. Guo, W. Wang, X. Xie, S. Wang, and Y. Zhang, “Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer,” Biomedicine \& Pharmacotherapy, vol. 159, p. 114256, Jan. 2023, doi: 10.1016/j.biopha.2023.114256.
|
||||
\bibitem{tabular}
|
||||
A. Nasimian, M. Ahmed, I. Hedenfalk, and J. U. Kazi, “A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer,” Computational and Structural Biotechnology Journal, vol. 21, pp. 956–964, doi: 10.1016/j.csbj.2023.01.020.
|
||||
\bibitem{deep}
|
||||
J. Longden et al., “Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space,” Cell Reports, vol. 34, no. 3, p. 108657, Jan. 2021, doi: 10.1016/j.celrep.2020.108657.
|
||||
\bibitem{cancer}
|
||||
International Agency for Research on Cancer, F. Bray, IARC, E. Weiderpass, and World Health Organization, “Latest global cancer data: Cancer burden rises to 19.3 million new cases and 10.0 million cancer deaths in 2020,” IARC, Dec. 15, 2020. \url{https://www.iarc.who.int/wp-content/uploads/2020/12/pr292_E.pdf} (accessed Dec. 01, 2024).
|
||||
\bibitem{therapy}
|
||||
Sh. Huang and B. O. Sullivan, “Oral cancer: Current role of radiotherapy and chemotherapy,” Medicina Oral, Patología Oral Y Cirugía Bucal, pp. e233–e240, Jan. 2013, doi: 10.4317/medoral.18772.
|
||||
\bibitem{treateoc}
|
||||
L. Kuroki and S. R. Guntupalli, “Treatment of epithelial ovarian cancer,” BMJ, p. m3773, Nov. 2020, doi: 10.1136/bmj.m3773.
|
||||
\bibitem{resistance}
|
||||
S. W. Johnson, R. F. Ozols, and T. C. Hamilton, “Mechanisms of drug resistance in ovarian cancer,” Cancer, vol. 71, no. S2, pp. 644–649, Aug. 2010, doi: 10.1002/cncr.2820710224.
|
||||
\bibitem{mlrole}
|
||||
Y. Jiang, M. Yang, S. Wang, X. Li, and Y. Sun, “Emerging role of deep learning‐based artificial intelligence in tumor pathology,” Cancer Communications, vol. 40, no. 4, pp. 154–166, Apr. 2020, doi: 10.1002/cac2.12012.
|
||||
\bibitem{shap}
|
||||
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” arXiv (Cornell University), Jan. 2017, doi: 10.48550/arxiv.1705.07874.
|
||||
\bibitem{lasso}
|
||||
R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society Series B (Statistical Methodology), vol. 58, no. 1, pp. 267–288, Jan. 1996, doi: 10.1111/j.2517-6161.1996.tb02080.x.
|
||||
\bibitem{cellprofile}
|
||||
C. McQuin et al., “CellProfiler 3.0: Next-generation image processing for biology,” PLoS Biology, vol. 16, no. 7, p. e2005970, Jul. 2018, doi: 10.1371/journal.pbio.2005970.
|
||||
\bibitem{tcga}
|
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“The Cancer Genome Atlas Program (TCGA),” Cancer.gov. \url{https://www.cancer.gov/ccg/research/genome-sequencing/tcga} (accessed Dec. 01, 2024).
|
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\bibitem{geo}
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||||
“Gene Expression Omnibus (GEO) Database.” \url{https://www.ncbi.nlm.nih.gov/geo/} (accessed Dec. 01, 2024).
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\bibitem{ega}
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||||
“EGA European Genome-Phenome Archive,” The European Bioinformatics Institute (EMBL-EBI). \url{https://ega-archive.org/} (accessed Dec. 01, 2024).
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||||
\bibitem{atcc}
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“ATCC: The Global Bioresource Center,” ATCC. \url{https://www.atcc.org/} (accessed Dec. 01, 2024).
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\bibitem{r-lang}
|
||||
“R: The R Project for Statistical Computing.” \url{https://www.r-project.org/} (accessed Dec. 01, 2024).
|
||||
\bibitem{dalex}
|
||||
P. Biecek, “DALEX: Explainers for Complex Predictive Models in R,” Zenodo (CERN European Organization for Nuclear Research), Feb. 2020, doi: 10.5281/zenodo.3670940.
|
||||
\bibitem{PerkinElmer}
|
||||
“PerkinElmer | Science with purpose.” \url{https://content.perkinelmer.com/} (accessed Dec. 01, 2024).
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\end{thebibliography}
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\end{document}
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