From b271a0b7f0984dc9511d3895d10051b044274d8d Mon Sep 17 00:00:00 2001 From: Arity-T Date: Thu, 5 Dec 2024 11:27:06 +0300 Subject: [PATCH] =?UTF-8?q?=D0=A1=D1=81=D1=8B=D0=BB=D0=BA=D0=B8=20=D0=B2?= =?UTF-8?q?=20=D0=BF=D0=BE=D1=80=D1=8F=D0=B4=D0=BA=D0=B5=20=D0=B8=D1=85=20?= =?UTF-8?q?=D1=83=D0=BF=D0=BE=D0=BC=D0=B8=D0=BD=D0=B0=D0=BD=D0=B8=D1=8F=20?= =?UTF-8?q?=D0=B2=20=D1=82=D0=B5=D0=BA=D1=81=D1=82=D0=B5?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Надо было использовать bib файл и всё бы само сортировалось --- report.tex | 64 +++++++++++++++++++++++++++--------------------------- 1 file changed, 32 insertions(+), 32 deletions(-) diff --git a/report.tex b/report.tex index 9c9f5a9..f129b70 100644 --- a/report.tex +++ b/report.tex @@ -276,26 +276,6 @@ \newpage \vspace{-1.5cm} \begin{thebibliography}{0} - \bibitem{paclitaxel} - 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} @@ -304,28 +284,48 @@ 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{paclitaxel} + L. 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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} - “The Cancer Genome Atlas Program (TCGA),” Cancer.gov. \url{https://www.cancer.gov/ccg/research/genome-sequencing/tcga} (accessed Dec. 01, 2024). - \bibitem{geo} - “Gene Expression Omnibus (GEO) Database.” \url{https://www.ncbi.nlm.nih.gov/geo/} (accessed Dec. 01, 2024). - \bibitem{ega} - “EGA European Genome-Phenome Archive,” The European Bioinformatics Institute (EMBL-EBI). \url{https://ega-archive.org/} (accessed Dec. 01, 2024). - \bibitem{atcc} - “ATCC: The Global Bioresource Center,” ATCC. \url{https://www.atcc.org/} (accessed Dec. 01, 2024). - \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{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{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{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{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{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{PerkinElmer} “PerkinElmer | Science with purpose.” \url{https://content.perkinelmer.com/} (accessed Dec. 01, 2024). + \bibitem{geo} + “Gene Expression Omnibus (GEO) Database.” \url{https://www.ncbi.nlm.nih.gov/geo/} (accessed Dec. 01, 2024). + \bibitem{tcga} + “The Cancer Genome Atlas Program (TCGA),” Cancer.gov. \url{https://www.cancer.gov/ccg/research/genome-sequencing/tcga} (accessed Dec. 01, 2024). + \bibitem{atcc} + “ATCC: The Global Bioresource Center,” ATCC. \url{https://www.atcc.org/} (accessed Dec. 01, 2024). + \bibitem{ega} + “EGA European Genome-Phenome Archive,” The European Bioinformatics Institute (EMBL-EBI). \url{https://ega-archive.org/} (accessed Dec. 01, 2024). + \bibitem{r-lang} + “R: The R Project for Statistical Computing.” \url{https://www.r-project.org/} (accessed Dec. 01, 2024). \end{thebibliography}