PCA-FA： Applying Supervised Learning to Analyze Gene Expression Data

In previous gene expression data analyses, supervised learning has mainly focused on the classification 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 intervalscaled attributes). The fuzzy FA was introduced to model the interval-scaled attributes. In this paper, an approximate linear relationship between gene expression data of lung adenocarcinoma patients and survival outcome is successfully revealed by PCA-TA.

PCA-FA:Applying Supervised Learning to Analyze Gene Expression Data

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.