摘要
In previous gene expression data analyses, supervised learning has mainly focused on the clas-sification of attribute data, such as the different experimental conditions, different known classes of the same tumor and sex. However, supervised learning classification is not suitable for interval-scaled attributes, such as age and survival outcome of cancer patients. For this problem, this paper proposed a new method by combining two well-known methods: principal component analysis (PCA) and Fisher analysis (FA). The method, PCA-FA, realizes supervised learning with two types of attributes (nominal attributes and interval-scaled attributes). The fuzzy FA was introduced to model the interval-scaled attributes. In this paper, an ap-proximate linear relationship between gene expression data of lung adenocarcinoma patients and survival outcome is successfully revealed by PCA-TA.
In previous gene expression data analyses, supervised learning has mainly focused on the clas-sification of attribute data, such as the different experimental conditions, different known classes of the same tumor and sex. However, supervised learning classification is not suitable for interval-scaled attributes, such as age and survival outcome of cancer patients. For this problem, this paper proposed a new method by combining two well-known methods: principal component analysis (PCA) and Fisher analysis (FA). The method, PCA-FA, realizes supervised learning with two types of attributes (nominal attributes and interval-scaled attributes). The fuzzy FA was introduced to model the interval-scaled attributes. In this paper, an ap-proximate linear relationship between gene expression data of lung adenocarcinoma patients and survival outcome is successfully revealed by PCA-TA.
