univer-5th-semester/literature-review
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Добавил про статью в machine learning

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Артём Тищенко
2024-11-30 20:15:54 +03:00
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The authors of~\cite{tabular} developed a machine learning model to predict cisplatin sensitivity based on gene expression changes induced by cisplatin treatment. They combined gene expression data from sensitive ovarian cancer cell lines and patients with specific signaling alterations to identify a gene signature. Using this signature, they trained TabNet, an interpretable deep learning algorithm for tabular data, to perform binary classification of sensitivity to cisplatin. Also several other machine learning algorithms, including Ridge, LASSO, Elastic Net, Nu-Support Vector Classification (Nu-SVC), XGBoost, and Random Forest, were applied to the same task for comparission with TabNet. The authors of~\cite{tabular} developed a machine learning model to predict cisplatin sensitivity based on gene expression changes induced by cisplatin treatment. They combined gene expression data from sensitive ovarian cancer cell lines and patients with specific signaling alterations to identify a gene signature. Using this signature, they trained TabNet, an interpretable deep learning algorithm for tabular data, to perform binary classification of sensitivity to cisplatin. Also several other machine learning algorithms, including Ridge, LASSO, Elastic Net, Nu-Support Vector Classification (Nu-SVC), XGBoost, and Random Forest, were applied to the same task for comparission with TabNet.
Same as in the~\cite{heterogeneity}, the authors of~\cite{deep} used algorithms from the specialized software called Acapella (developed by PerkinElmer~\cite{PerkinElmer}) to extract 624 quantitative image features from cellular images. This information was fed into a deep learning model that identified a continuous 27-dimension space describing all of the observed cell morphologies. After that the random forest classifier was trained on populations of cells labeled as either drug sensitive or drug resistant.
\section{Datasets} \section{Datasets}
Data plays a crucial role in machine learning, serving as the foundation for model training and evaluation. The quality and quantity of data directly influence the performance and generalizability of machine learning algorithms. In the fields of biology and medicine, data collection is often costly and time-consuming. Additionally, the complexity and variability inherent in biological systems further complicate data acquisition and interpretation. In cancer research, these challenges are even more pronounced due to the heterogeneity of tumors and the intricate nature of cancer biology. However, there are valuable resources available, such as the Gene Expression Omnibus (GEO) database~\cite{geo} and The Cancer Genome Atlas (TCGA) database~\cite{tcga}, which provide researchers with access to extensive datasets. Moreover, nonprofit organizations like the American Type Culture Collection (ATCC)~\cite{atcc} enable researchers to obtain biological materials, including cancer cells. Data plays a crucial role in machine learning, serving as the foundation for model training and evaluation. The quality and quantity of data directly influence the performance and generalizability of machine learning algorithms. In the fields of biology and medicine, data collection is often costly and time-consuming. Additionally, the complexity and variability inherent in biological systems further complicate data acquisition and interpretation. In cancer research, these challenges are even more pronounced due to the heterogeneity of tumors and the intricate nature of cancer biology. However, there are valuable resources available, such as the Gene Expression Omnibus (GEO) database~\cite{geo} and The Cancer Genome Atlas (TCGA) database~\cite{tcga}, which provide researchers with access to extensive datasets. Moreover, nonprofit organizations like the American Type Culture Collection (ATCC)~\cite{atcc} enable researchers to obtain biological materials, including cancer cells.
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The R Project for Statistical Computing. Available at \url{https://www.r-project.org/}. Accessed October 8, 2024. The R Project for Statistical Computing. Available at \url{https://www.r-project.org/}. Accessed October 8, 2024.
\bibitem{dalex} \bibitem{dalex}
DALEX: explainers for complex predictive models, Przemyslaw Biecek, 2018. DALEX: explainers for complex predictive models, Przemyslaw Biecek, 2018.
\bibitem{PerkinElmer}
PerkinElmer official website. Available at \url{https://content.perkinelmer.com/}. Accessed October 8, 2024.
\end{thebibliography} \end{thebibliography}
\end{document} \end{document}