Traditional treatment selection of cancers mainly relies on clinical observations and doctor’s judgment, but most outcomes can hardly be predicted. Through Genomics Topology, we use 272 breast cancer patients’ clini...Traditional treatment selection of cancers mainly relies on clinical observations and doctor’s judgment, but most outcomes can hardly be predicted. Through Genomics Topology, we use 272 breast cancer patients’ clinical and gene information as an example to propose a treatment optimization and top gene identification system. This study faces certain challenges such as collinearity and the Curse of Dimensionality within data, so by the idea of Analysis of Variance (ANOVA), Principal Component Analysis (PCA) is implemented to resolve this issue. Several genes, for example, SLC40A1 and ACADSB, are found to be both statistically significant and biological-studies supported;the model developed can precisely predict breast cancer mortality, recurrence time, and survival time, with an average MSE of 3.697, accuracy rate of 88.97%, and F1 score of 0.911. The result and methodology used in this study provide a channel for people to further look into the more precise prediction of other cancer outcomes through machine learning and assist in the discovery of targetable pathways for next-generation cancer treatment methods.展开更多
Cancer genomic research is a relatively new method. It has shown great potential but faces certain challenges. Researchers often have to deal with tens of thousands of genes with a relatively small sample size of pati...Cancer genomic research is a relatively new method. It has shown great potential but faces certain challenges. Researchers often have to deal with tens of thousands of genes with a relatively small sample size of patient cases—a dilemma referred to as the “Curse of Dimensionality” [1]—and it makes it hard to learn the data well because of relatively sparse data in high dimensional space. To deal with the dilemma, this study uses p-values of individual genes for pathway enrichment to find statistically significant pathways. The aim of this study is to find significant genes and biological pathways that are related to lung cancer by statistical method and pathway enrichment analysis. Several significant genes, such as WNT2B, VAV2, and significant pathways, such as Metabolism of xenobiotics by cytochrome P450-Homo sapiens (human) and Fatty acid degradation-Homo sapiens (human), are found to be both statistically significant and biological studies supported. Significant genes-including TESK2, C5orf43, and ZSCAN21—and significant pathways such as Pentose and glucoronate interconversions-Homo sapiens (human), are found to be new cancer-related genes and pathways that worth laboratory studies. The idea and method used in this research can be applied to find more significant genes and pathways that worth study experimentally.展开更多
文摘Traditional treatment selection of cancers mainly relies on clinical observations and doctor’s judgment, but most outcomes can hardly be predicted. Through Genomics Topology, we use 272 breast cancer patients’ clinical and gene information as an example to propose a treatment optimization and top gene identification system. This study faces certain challenges such as collinearity and the Curse of Dimensionality within data, so by the idea of Analysis of Variance (ANOVA), Principal Component Analysis (PCA) is implemented to resolve this issue. Several genes, for example, SLC40A1 and ACADSB, are found to be both statistically significant and biological-studies supported;the model developed can precisely predict breast cancer mortality, recurrence time, and survival time, with an average MSE of 3.697, accuracy rate of 88.97%, and F1 score of 0.911. The result and methodology used in this study provide a channel for people to further look into the more precise prediction of other cancer outcomes through machine learning and assist in the discovery of targetable pathways for next-generation cancer treatment methods.
文摘Cancer genomic research is a relatively new method. It has shown great potential but faces certain challenges. Researchers often have to deal with tens of thousands of genes with a relatively small sample size of patient cases—a dilemma referred to as the “Curse of Dimensionality” [1]—and it makes it hard to learn the data well because of relatively sparse data in high dimensional space. To deal with the dilemma, this study uses p-values of individual genes for pathway enrichment to find statistically significant pathways. The aim of this study is to find significant genes and biological pathways that are related to lung cancer by statistical method and pathway enrichment analysis. Several significant genes, such as WNT2B, VAV2, and significant pathways, such as Metabolism of xenobiotics by cytochrome P450-Homo sapiens (human) and Fatty acid degradation-Homo sapiens (human), are found to be both statistically significant and biological studies supported. Significant genes-including TESK2, C5orf43, and ZSCAN21—and significant pathways such as Pentose and glucoronate interconversions-Homo sapiens (human), are found to be new cancer-related genes and pathways that worth laboratory studies. The idea and method used in this research can be applied to find more significant genes and pathways that worth study experimentally.
