univer-5th-semester/literature-review
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\vspace{-1.5cm}
\begin{thebibliography}{0}
\bibitem{paclitaxel}
Lu Xin, Wen Xiao, Huanzhi Zhang, Yakun Liu, Xiaoping Li, Pietro Ferraro, Feng Pan, Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning, 2024.
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}
Qiuli Zhu, Hua Dai, Feng Qiu, Weiming Lou, Xin Wang, Libin Deng, Chao Shi, Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer, 2024.
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}
Ziyu Liu, Zahra Zeinalzadeh, Tao Huang, Yingying Han, Lushan Peng, Dan Wang, Zongjiang Zhou, DIABATE Ousmane, Junpu Wang, 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, 2024.
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}
Jun Zhang, Youliang Weng, Yi Liu, Nan Wang, Shangyuan Feng, Sufang Qiu, Duo Lin, Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction, 2024.
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}
Shen S, Wang C, Gu J, Song F, Wu X, Qian F, Chen X, Wang L, Peng Q, Xing Z, Gu L, Wang F, Cheng X. A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles, 2024.
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}
Xiandong Lin, QingLan Ma, Lei Chen, Wei Guo, Zhiyi Huang, Tao Huang, Yu-Dong Cai, Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods, 2023.
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}
Si-Yuan Lu, Qiong-Cong Xu, De-Liang Fang, Yin-Hao Shi, Ying-Qin Zhu, Zhi-De Liu, Ming-Jian Ma, Jing-Yuan Ye, Xiao Yu Yin, Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance, 2024.
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}
Lu Guo, Wei Wang, Xiaodong Xie, Shuihua Wang, Yudong Zhang, Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer, 2023.
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}
Ahmad Nasimian, Mehreen Ahmed, Ingrid Hedenfalk, Julhash U. Kazi, A deep tabular data learning model predicting cisplatin sensitivity identifies BCL2L1 dependency in cancer, 2023.
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}
James Longden, Xavier Robin, Mathias Engel, Jesper Ferkinghoff-Borg, Ida Kjær, Ivan D. Horak, Mikkel W. Pedersen, Rune Linding, Deep neural networks identify signaling mechanisms of ErbB-family drug resistance from a continuous cell morphology space, 2021.
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{cellprofile}
T. Misteli, C. McQuin, A. Goodman, V. Chernyshev, L. Kamentsky, B.A. Cimini, et al., CellProfiler 3.0: next-generation image processing for biology, 2018.
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}
The Cancer Genome Atlas (TCGA) database. Available at \url{https://www.cancer.gov/ccg/research/genome-sequencing/tcga}. Accessed October 8, 2024.
“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. Available at \url{https://www.ncbi.nlm.nih.gov/geo/}. Accessed October 8, 2024.
“Gene Expression Omnibus (GEO) Database.” \url{https://www.ncbi.nlm.nih.gov/geo/} (accessed Dec. 01, 2024).
\bibitem{ega}
The European Genome-phenome Archive (EGA). Available at \url{https://ega-archive.org/}. Accessed October 8, 2024.
“EGA European Genome-Phenome Archive,” The European Bioinformatics Institute (EMBL-EBI). \url{https://ega-archive.org/} (accessed Dec. 01, 2024).
\bibitem{atcc}
American Type Culture Collection (ATCC). Available at \url{https://www.atcc.org/}. Accessed October 8, 2024.
“ATCC: The Global Bioresource Center,” ATCC. \url{https://www.atcc.org/} (accessed Dec. 01, 2024).
\bibitem{r-lang}
The R Project for Statistical Computing. Available at \url{https://www.r-project.org/}. Accessed October 8, 2024.
“R: The R Project for Statistical Computing.” \url{https://www.r-project.org/} (accessed Dec. 01, 2024).
\bibitem{dalex}
DALEX: explainers for complex predictive models, Przemyslaw Biecek, 2018.
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 official website. Available at \url{https://content.perkinelmer.com/}. Accessed October 8, 2024.
“PerkinElmer | Science with purpose.” \url{https://content.perkinelmer.com/} (accessed Dec. 01, 2024).
\end{thebibliography}
\end{document}