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kras в ml и datasets

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Артём Тищенко
2024-11-04 17:34:46 +03:00
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In~\cite{platinum}, the authors performed differential protein analysis on the expression profiles of 745 proteins related to platinum-based chemotherapy resistance. They used LASSO regression to select 10 proteins linked to chemotherapy outcomes, followed by univariate logistic regression on nine clinical factors. Variables with p < 0.1 were included in a multivariate logistic regression analysis, resulting in four significant variables: three proteins and one clinical parameter (postoperative residual tumor). This analysis enabled the construction of a predictive machine-learning model for chemotherapy resistance in patients with EOC.
The authors of article~\cite{kras} applied machine learning algorithms for two goals. Firstly, they used algorithms to extract genes highly related with therapy resistance. Each sample of their data contained the expression of 8687 genes and only a small portion was correlated with targeted therapy resistance. To extract highly related genes in this study authors attempted seven algorithms, 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 (CATBoost), and eXtreme Gradient Boosting (XGBoost). Secondly, they selected four algorithms to perform binary classification (resistant vs sensitive) of tumor cells based on extracted features, namely, random forest (RF), support vector machine (SVM), K-Nearest Neighbors (KNN), and decision tree (DT).
\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.
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obtained from Fujian Provincial Cancer Hospital. As well as in~\cite{paclitaxel}, authors used unique method called surface enhanced Raman spectroscopy (SERS) to extract molecular profiles of patients plasma. Authors even claim that SERS based on
surface plasmon resonance was used for this task for the first time. The SERS spectra were processed by deducting the fluorescence background signal using a fifth-order polynomial fitting method, and then the SERS signals were peak normalized, after which the spectra of the same plasma sample were averaged to represent the final SERS data for that sample.э
Authors of articles~\cite{heterogeneity} and~\cite{mitochondria} turned to open databases to prepare datasets for their research. Authors of~\cite{heterogeneity} downloaded frozen histopathologic images of 494 ovarian and 70 paracarcinoma tissues with hematoxylin–eosin (HE) staining from TCGA~\cite{tcga}. The corresponding clinical information, genomics, and transcriptomics profiles required for this study were also obtained from this database. Authors of~\cite{mitochondria} also used TCGA. They downloaded information on 183 esophageal cancer patients (95 squamous cell carcinomas and 88 adenocarcinomas) was obtained, including mRNA expression profiles, clinical features such as survival time and status, age, gender, and pathological stage (T, N, and M). Additionally authors used Gene Expression Omnibus (GEO) database~\cite{geo}. RNA sequencing (RNA-seq) for GSE45670 was downloaded from it. GSE45670 includes a total of 17 esophageal squamous cell carcinomas (ESCC) that did not respond to preoperative CRT, 11 ESCC that responded to preoperative CRT, and 10 samples from normal esophageal epithelium. The GEO dataset GSE53625 comprises 358 samples, including 179 ESCC tissue samples and an equal number of samples of adjacent normal tissues, along with detailed clinical data for the 179 ESCC patients. The GEO dataset GSE19417 contains data from 76 esophageal adenocarcinoma patients, offering detailed clinical data for 48 of these patients
Authors of articles~\cite{heterogeneity}, \cite{mitochondria} and~\cite{kras} turned to open databases to prepare datasets for their research. Authors of~\cite{heterogeneity} downloaded frozen histopathologic images of 494 ovarian and 70 paracarcinoma tissues with hematoxylin–eosin (HE) staining from TCGA~\cite{tcga}. The corresponding clinical information, genomics, and transcriptomics profiles required for this study were also obtained from this database. Authors of~\cite{mitochondria} also used TCGA. They downloaded information on 183 esophageal cancer patients (95 squamous cell carcinomas and 88 adenocarcinomas) was obtained, including mRNA expression profiles, clinical features such as survival time and status, age, gender, and pathological stage (T, N, and M). Additionally authors used Gene Expression Omnibus (GEO) database~\cite{geo}. RNA sequencing (RNA-seq) for GSE45670 was downloaded from it. GSE45670 includes a total of 17 esophageal squamous cell carcinomas (ESCC) that did not respond to preoperative CRT, 11 ESCC that responded to preoperative CRT, and 10 samples from normal esophageal epithelium. The GEO dataset GSE53625 comprises 358 samples, including 179 ESCC tissue samples and an equal number of samples of adjacent normal tissues, along with detailed clinical data for the 179 ESCC patients. The GEO dataset GSE19417 contains data from 76 esophageal adenocarcinoma patients, offering detailed clinical data for 48 of these patients. Authors of~\cite{kras} also took gene expression profile data from GEO database, specifically from accession number GSE137912. Their analysis involved 7612 samples treated with KRAS G12C inhibitors. Among these samples, 4297 were tumor cells that persisted in proliferation, whereas 3315 were tumor cells that had ceased proliferating. Each sample contained the expression of 8687 genes.
In article~\cite{platinum}, authors prepared their own dataset and also used open databases. In this study, 4D data-independent acquisition (DIA) proteomic sequencing was performed on tissue-derived extracellular vesicles (tsEVs) obtained from 58 platinum-sensitive and 30 platinum-resistant patients with EOC. Also authors used the GSE15372, GSE33482, GSE26712 and GSE63885 microarray datasets from the Gene Expression Omnibus database~\cite{geo}. GSE15372 and GSE33482 represent EOC cell line-derived RNA microarray datasets, comprising 5 and 5 and 6 and 6 platinum-sensitive and resistant cell line samples, respectively. GSE26712 and GSE63885 involve clinical and sequencing data for 195 and 101 EOC patients, respectively. Additionally, transcriptomic sequencing data and clinical information from the tumour tissues of 379 patients with EOC, sourced from the TCGA database~\cite{tcga}, was used.