univer-5th-semester/literature-review
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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~\cite{mitochondria} & Esophageal cancer & Generalized linear model (GLM), K-nearest neighbor (KNN), least absolute shrinkage and selection operator (LASSO) regression, neural network (NN), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGB) & Nearly 500 tissue samples, RNA-sequences and some other clinical data from Gene Expression Omnibus (GEO) database~\cite{geo}, information on 183 esophageal cancer patients from The Cancer Genome Atlas (TCGA) database~\cite{tcga} & Statistical analysis using DALEX package~\cite{dalex} for~R~\cite{r-lang} \\
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Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~\cite{sers} & Nasopharyng-eal cancer & Principal component analysis and linear discriminant analysis (PCA-LDA) & Self-produced dataset of 120 plasma samples, 60 of which from healthy volunteers, 30 from radiotherapy sensitivity patients and 30 from radiotherapy resistance patients & - \\
A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~\cite{platinum} & Epithelial ovarian cancer (EOC) & Least absolute shrinkage and selection operator (LASSO) regression, logistic regression (LR) & Nearly 300 tissue samples, and other clinical data from Gene Expression Omnibus (GEO) database~\cite{geo}, transcriptomic sequencing data and clinical information from tumor tissues of 379 EOC patients from The Cancer Genome Atlas (TCGA) database~\cite{tcga} & \\
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\centering
\caption{Methods used in research papers (continued).}
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\hline
\textbf{Article} & \textbf{Cancer type} & \textbf{Machine learning algorithms} & \textbf{Datasets} & \textbf{Feature importance analysis} \\
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A Predictive Model for Initial Platinum-Based Chemotherapy Efficacy in Patients with Postoperative Epithelial Ovarian Cancer Using Tissue-Derived Small Extracellular Vesicles~\cite{platinum} & Epithelial ovarian cancer (EOC) & Least absolute shrinkage and selection operator (LASSO) regression, logistic regression (LR) & Nearly 300 tissue samples, and other clinical data from Gene Expression Omnibus (GEO) database~\cite{geo}, transcriptomic sequencing data and clinical information from tumor tissues of 379 EOC patients from The Cancer Genome Atlas (TCGA) database~\cite{tcga} & - \\
Molecular separation-assisted label-free SERS combined with machine learning for nasopharyngeal cancer screening and radiotherapy resistance prediction~\cite{sers} & Nasopharyng-eal cancer & Principal component analysis and linear discriminant analysis (PCA-LDA) & Self-produced dataset of 120 plasma samples, 60 of which from healthy volunteers, 30 from radiotherapy sensitivity patients and 30 from radiotherapy resistance patients & \\
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Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~\cite{kras} & Lung cancer & Random forest (RF), support vector machine (SVM), K-nearest neighbors (KNN), decision tree (DT) & 7612 sample of gene expression profile data from Gene Expression Omnibus (GEO) database~\cite{geo}. Each sample contained the expression of 8687 genes & Seven feature ranking algorithms were applied, including least absolute shrinkage and selection operator (LASSO), light gradient boosting machine (LightGBM), monte carlo feature selection (MCFS), minimum redundancy maximum relevance (mRMR), random forest (RF) - based, categorical boosting (CATB), and extreme gradient boosting (XGB) \\
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Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance~\cite{glut} & Pancreatic cancer & Support vector machine (SVM), CoxBoost, random forest (RF), least absolute shrinkage and selection operator (LASSO), stepwise Cox, partial least squares regression for Cox (plsRcox), Ridge, supervised principal components (SuperPC), elastic network (Enet), generalized boosted regression modeling (GBM) & Nearly 1000 samples from 12 datasets from The Cancer Genome Atlas (TCGA)~\cite{tcga}, Gene Expression Omnibus (GEO)~\cite{geo} and The European Genome-phenome Archive (EGA)~\cite{ega} & The univariate Cox regression analysis was used to identify immunosenescence-related genes with prognostic significance in pancreatic cancer. Genes with a p-value of less than 0.01 were selected as meaningful features for subsequent analysis \\
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Identifying genes associated with resistance to KRAS G12C inhibitors via machine learning methods~\cite{kras} & Identified some top-ranked genes, including H2AFZ, CKS1B, TUBA1B, RRM2, and BIRC5, associated with cancer progression and drug resistance. Have built efficient classifiers as the byproduct & Categorical boosting (CATB) for feature selection and support vector machine (SVM) for classification & Accuracy of 93.1\% and F1-score of 0.938 \\
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Turning to immunosuppressive tumors: Deciphering the immunosenescence-related microenvironment and prognostic characteristics in pancreatic cancer, in which GLUT1 contributes to gemcitabine resistance~\cite{glut} & Identified that IMSP1 and IMSP2 phenotypes influence pancreatic cancer prognosis and treatment response. Found that high MLIRS scores are linked to lower immune infiltration, while low scores indicate better drug sensitivity. Highlighted GLUT1 as a key factor driving tumor proliferation, migration, and chemotherapy resistance & Stepwise Cox combined with generalized boosted regression modeling (GBM) & Area under the curve (AUC) of 0.91 \\
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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.
\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.
\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.
\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.
\bibitem{tcga}
The Cancer Genome Atlas (TCGA) database. Available at \url{https://www.cancer.gov/ccg/research/genome-sequencing/tcga}. Accessed October 8, 2024.
\bibitem{geo}
Gene Expression Omnibus (GEO) database. Available at \url{https://www.ncbi.nlm.nih.gov/geo/}. Accessed October 8, 2024.
\bibitem{ega}
The European Genome-phenome Archive (EGA). Available at \url{https://ega-archive.org/}. Accessed October 8, 2024.
\bibitem{atcc}
American Type Culture Collection (ATCC). Available at \url{https://www.atcc.org/}. Accessed October 8, 2024.
\bibitem{r-lang}