univer-5th-semester/literature-review
Code Actions Releases 3

Добавил в results

Browse Source
This commit is contained in:
Артём Тищенко
2024-11-30 20:37:50 +03:00
parent 0fdf67a02a
commit 282eb0bd18
1 changed files with 3 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

4
report.tex
View File

@@ -190,7 +190,7 @@
\section{Results} \section{Results}
In all works, the construction of machine learning models is essentially a secondary result. First of all, studies show the applicability of these methods to tasks related to the problems of cancer cell resistance to chemotherapy. Also, using machine learning methods, the authors test their hypotheses, confirm or discover links between various characteristics of cancer cells, patient clinical data and drug resistance. In all works, the construction of machine learning models is essentially a secondary result. First of all, studies show the applicability of these methods to tasks related to the problems of cancer cell resistance to chemotherapy. Also, using machine learning methods, the authors test their hypotheses, confirm or discover links between various characteristics of cancer cells, patient clinical data and drug resistance.
In articles \cite{paclitaxel}, \cite{sers}, \cite{platinum}, \cite{kras}, \cite{cervical}, \cite{tabular}, the authors try to solve the problem of determining drug resistance directly. In \cite{sers}, \cite{platinum}, \cite{kras}, \cite{cervical}, \cite{tabular}, the problem of binary classification (drug resistant vs drug sensitive) is solved, and in \cite{paclitaxel}, cells are classified into 4 classes, which constitute a gradation of the level of resistance of cancer cells to chemotherapy. In articles \cite{paclitaxel}, \cite{sers}, \cite{platinum}, \cite{kras}, \cite{cervical}, \cite{tabular}, \cite{deep}, the authors try to solve the problem of determining drug resistance directly. In \cite{sers}, \cite{platinum}, \cite{kras}, \cite{cervical}, \cite{tabular}, \cite{deep}, the problem of binary classification (drug resistant vs drug sensitive) is solved, and in \cite{paclitaxel}, cells are classified into 4 classes, which constitute a gradation of the level of resistance of cancer cells to chemotherapy.
In \cite{paclitaxel}, five different machine learning algorithms were compared, the best results were achieved using support vector machine (accuracy of 93.4\%) and neural network (accuracy of 94.5\%). The classification was based on morphological features and, by constructing effective classifiers, the authors demonstrated that these features are directly related to the level of resistance of cancer cells to chemotherapy. Also, using SHapley Additive exPlanations authors showed that only a 25 of 112 features are really important for the classification. In \cite{paclitaxel}, five different machine learning algorithms were compared, the best results were achieved using support vector machine (accuracy of 93.4\%) and neural network (accuracy of 94.5\%). The classification was based on morphological features and, by constructing effective classifiers, the authors demonstrated that these features are directly related to the level of resistance of cancer cells to chemotherapy. Also, using SHapley Additive exPlanations authors showed that only a 25 of 112 features are really important for the classification.
@@ -204,6 +204,8 @@
The authors of \cite{tabular} developed a deep learning model using the TabNet algorithm to predict cisplatin sensitivity based on cisplatin-perturbed gene expression data. Their model achieved over 80\% accuracy, surpassing a variety of other machine learning algorithms such as ridge regression, lasso, elastic net, Nu-SVC, XGBoost, and random forest. The TabNet model consistently demonstrated strong predictive performance with an average AUC of 0.808 across 500 different sample splits. By analyzing feature importance, the authors identified several key genes contributing to cisplatin resistance, most notably BCL2L1. The upregulation of BCL2L1, along with genes like CCND1 and PLK2, was associated with poor survival in ovarian cancer patients, highlighting potential targets for overcoming drug resistance. These findings are in line with the results of \cite{kras}, where important genes associated with tumor progression and drug resistance were also identified using machine learning feature selection techniques. The authors of \cite{tabular} developed a deep learning model using the TabNet algorithm to predict cisplatin sensitivity based on cisplatin-perturbed gene expression data. Their model achieved over 80\% accuracy, surpassing a variety of other machine learning algorithms such as ridge regression, lasso, elastic net, Nu-SVC, XGBoost, and random forest. The TabNet model consistently demonstrated strong predictive performance with an average AUC of 0.808 across 500 different sample splits. By analyzing feature importance, the authors identified several key genes contributing to cisplatin resistance, most notably BCL2L1. The upregulation of BCL2L1, along with genes like CCND1 and PLK2, was associated with poor survival in ovarian cancer patients, highlighting potential targets for overcoming drug resistance. These findings are in line with the results of \cite{kras}, where important genes associated with tumor progression and drug resistance were also identified using machine learning feature selection techniques.
In \cite{deep} authors demonstated how deep learning of cell morphologies can be used to successfully predict drug resistance state in cancer cell lines from diverse tissues. They built a classifier based on deep neural network and random forest which can identify cancer cells resistance to ErbB-family drugs with an accuracy of 74\%.
In articles \cite{heterogeneity}, \cite{mitochondria}, \cite{glut}, the authors used machine learning for a different tasks. In~\cite{heterogeneity} used machine learning algorithms from the specialized software CellProfiler~\cite{cellprofile} to extract quantitative image features and then performed statistical analysis of feature importance. The authors of~\cite{mitochondria} and~\cite{glut} applied machine learning algorithms for the regression task and proposed their own scores, mitochondria related chemoradiotherapy resistance (MRCRTR) score and machine learning-derived immunosenescence-related score (MLIRS), respectively. In articles \cite{heterogeneity}, \cite{mitochondria}, \cite{glut}, the authors used machine learning for a different tasks. In~\cite{heterogeneity} used machine learning algorithms from the specialized software CellProfiler~\cite{cellprofile} to extract quantitative image features and then performed statistical analysis of feature importance. The authors of~\cite{mitochondria} and~\cite{glut} applied machine learning algorithms for the regression task and proposed their own scores, mitochondria related chemoradiotherapy resistance (MRCRTR) score and machine learning-derived immunosenescence-related score (MLIRS), respectively.
The study \cite{heterogeneity} demonstrated that specific computational pathomic signatures extracted from histopathological images can effectively predict drug resistance in ovarian cancer patients. By analyzing 1212 statistical image features derived from whole-slide images, the authors identified 26 key features related to patient survival. Among these, the Perimeter.sd feature, which measures the standard deviation of nuclear perimeter, stood out as the most significant predictor. A higher Perimeter.sd value was positively correlated with increased intra-tumor heterogeneity and was associated with a higher risk of platinum-based chemotherapy resistance. The study \cite{heterogeneity} demonstrated that specific computational pathomic signatures extracted from histopathological images can effectively predict drug resistance in ovarian cancer patients. By analyzing 1212 statistical image features derived from whole-slide images, the authors identified 26 key features related to patient survival. Among these, the Perimeter.sd feature, which measures the standard deviation of nuclear perimeter, stood out as the most significant predictor. A higher Perimeter.sd value was positively correlated with increased intra-tumor heterogeneity and was associated with a higher risk of platinum-based chemotherapy resistance.