The study of ovarian carcinoma gene regulatory mechanism based on the shortest path algorithm and conditional probabilities

In the current cancer research field, microarray is one of the most commonly used tools. It has the advantage of containing a large amount of data, which helped us in recording gene expressions in cancer and comparing the difference between normal cells and cancer cells. However, contemporary cancer research does not have a positive definition in how to analyze the microarray data. In this essay, we utilize the microarray data from the carcinoma cancer as primary sample. And we apply statistical methods and mathematical calculations to establish a diseases analyzing model. At first, we use principal component analysis to process the pre-selected data. Then we use ANOVA to select the genes with significant expression differences to be our target genes. Finally we use supervised learning method to evaluate the accuracy of classification. This analytical model helps to reduce the number of incorrect attempts and cut down the time which has to be spent in experiments. This model can analyze complicated cancer expression data, and it can also be useful in researches for other diseases.