univer-5th-semester/literature-review
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methods table

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Артём Тищенко
2024-10-15 19:14:57 +03:00
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report.tex
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\documentclass[a4paper, final]{article}
%\usepackage{literat} % Нормальные шрифты
\usepackage[14pt]{extsizes} % для того чтобы задать нестандартный 14-ый размер шрифта
\usepackage{tabularx}
\usepackage[T2A]{fontenc}
\usepackage[utf8]{inputenc}
% \usepackage[russian]{babel}
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\addcontentsline{toc}{section}{Introduction}
Progress has been made in chemotherapy drugs, but drug resistance remains a major challenge in cancer treatment and the main cause of cancer progression and even death. However, there are no clear indicators for predicting the risk of drug resistance in patients. Existing drug sensitivity assessment methods has limitations such as low modeling success rates, high cost, and time-consuming process. Machine learning is both an expanding and evolving field of computing, and it seems that it can significantly help in solving chemotherapy resistance problem. Here we provide an overview of how different studies apply machine learning algorithms to predict and understand chemotherapy resistance in various cancer types. Also we consider the strengths and limitations of each approach and discuss obtained results.
% \newpage
\newpage
\begin{table}[h!]
\centering
\caption{Methods used in research papers.}
\footnotesize
\begin{tabularx}{\textwidth}{|X|p{2cm}|X|X|X|}
\hline
\textbf{Article} & \textbf{Cancer type} & \textbf{Machine learning algorithms} & \textbf{Datasets} & \textbf{Feature importance analysis} \\
\hline
Classification of paclitaxel-resistant ovarian cancer cells using holographic flow cytometry through interpretable machine learning~\cite{paclitaxel} & Epithelial ovarian cancer (EOC) & Tree, Naive Bayes, K-nearest neighbors
(KNN), support vector machine (SVM), and neural network (NN) & Self-produced dataset of 2998 quantitative phase images (QPIs) of EOC cells & SHapley Additive
exPlanations (SHAP), Pearson coefficient, Kruskal-Wallis test \\
\hline
Heterogeneity of computational pathomic signature predicts drug resistance and intra-tumor heterogeneity of ovarian cancer~\cite{heterogeneity} & Epithelial ovarian cancer (EOC) & CellProfiler~\cite{cellprofile}, least absolute shrinkage and selection operator (LASSO) regression & 494 ovarian and 70 paracarcinoma tissues images from The Cancer Genome Atlas (TCGA) database~\cite{tcga} & Statistical analysis using R~\cite{r-lang}. Various visualizations, including heatmaps, Venn diagrams, ROC curves, and survival curves. \\
\hline
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} \\
\hline
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 & - \\
\hline
\end{tabularx}
\end{table}
% \section {Первый раздел}
% \subsection{Первый подраздел}
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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.
\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.
\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{r-lang}
The R Project for Statistical Computing. Available at \url{https://www.r-project.org/}. Accessed October 8, 2024.
\bibitem{dalex}
DALEX: explainers for complex predictive models, Przemyslaw Biecek, 2018.
\end{thebibliography}
\end{document}