ML section

report.tex

@@ -131,6 +131,27 @@
 
     \newpage
 
+    \section{Machine learning and chemotherapy resistance}
+    Machine learning has been widely applied to various classification, regression, feature extraction and many other problems in the field of biology and medicine. The field of cancer treatment has also not been left aside, in particular, machine learning has recently been actively used in research related to the problem of cancer cell chemotherapy resistance.
+    
+    Authors of~\cite{paclitaxel} applied and compared five different machine learning algorithms to classify cancer cells based on their level of drug resistance. They extracted 112 morphological features from dataset of nearly 3000 single-cell quantitative phase images of epithelial ovarian cancer (EOC) cells. After that, authors employed five supervised machine learning algorithms, Tree, Naive Bayes, K-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN), to perform multi-classification on four types of drug-resistant cancer cells. The optimal classification algorithm was determined by comparing the classification testing accuracy for each cell type and the confusion matrix. The chosen trained model was then used for further interpretable analysis.
+
+    Another study aims to evaluate the potential of mitochondria-related chemoradiotherapy (CRT) resistance (MRCRTR) genes in predicting esophageal cancer prognosis using machine learning \cite{mitochondria}. Authors used machine learning algorithms for both classification and regression tasks. For classification they applied seven algorithms: 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). They applied those algorithms to pretty similiar task as in~\cite{paclitaxel}, but in this paper authors identified only two classes -- CRT response and CRT non-response. The authors did not stop at classification alone, but also trained 10 machine learning algorithms, including random survival forest (RSF), elastic network (Enet), LASSO, ridge, stepwise Cox, Coxboost, partial least squares regression for Cox (plsRcox), supervised principal components (SuperPC), generalized boosted regression modeling (GBM), and survival support vector machine (survival-SVM), to build consensus prognostic model to predict MRCRTR score. Using the leave-one-out cross-validation (LOOCV) framework, a total of 101 algorithm combinations were applied to match prognostic models.
+
+    Machine learning algorithms also was successfully applied for same classification task as in~\cite{paclitaxel} and~\cite{mitochondria} by authors of~\cite{sers}. They employed robust machine learning algorithm based on principal component analysis and linear discriminant analysis (PCA-LDA)  to extract the feature of blood-SERS data and establish an effective predictive model for identifying the radiotherapy resistance subjects from sensitivity ones, and for identifying the nasopharyngeal cancer (NPC) subjects from healthy ones.
+
+    The authors of article~\cite{heterogeneity} chose a different approach by applying machine learning algorithms from the specialized software CellProfiler~\cite{cellprofile} to extract quantitative image features. They subsequently used bioinformatics analysis to explore the relationship between these features of intra-tumor heterogeneity (ITH) and drug resistance. Notably, the authors did not aim to train new models but instead utilized pre-trained algorithms from CellProfiler. Unlike studies \cite{paclitaxel}, \cite{mitochondria}, and \cite{sers}, where algorithms were employed for regression and classification tasks, this research focused specifically on extracting quantitative features from images. Based on CellProfiler, the authors constructed a pipeline for the extraction and analysis of these features, which enabled them to draw conclusions regarding the connection between these features and drug resistance in cancer cells.
+
+    \section{Feature analysis}
+
+    \section{Datasets}
+    
+    
+    \section{Results}
+
+
+
     \begin{table}[h!]
         \centering
         \caption{Methods used in research papers.}
@@ -174,14 +195,14 @@
         \end{tabularx}
     \end{table}
     
-    % \section {Первый раздел}
-    % \subsection{Первый подраздел}
-    % Текст первого подраздела
-
     % \section*{Conclusion}
     % \addcontentsline{toc}{section}{Conclusion}
     % Conclusion text
 
+    \newpage
+    \phantom{text}
+    \newpage
+    \phantom{text}
     \newpage
     % \section*{Literature}
     % \addcontentsline{toc}{section}{Literature}