scholarly journals Boolean Rule Based Classification for Microarray Gene Expression Data

2019 ◽  
Vol 8 (3) ◽  
pp. 5366-5370

Microarray technology provides a way to identify the expression level of ten thousands of genes simultaneously. This is useful for prediction and decision for the cancer treatments. To analyze and classify the gene expression data is more complex task. The rule based classifications are used to simplify the task of classifying genes. In this paper, a novel Boolean Rule based Classification (BRC) algorithm has been proposed. The efficient and relevant Boolean rules are assisting in classifying the test data correctly by Boolean Rule based Classifier model. This model is useful for drug designers. The experimental results show that in many cases the Boolean rule based classification yields more accurate results than other classical approaches

Author(s):  
Qiang Zhao ◽  
Jianguo Sun

Statistical analysis of microarray gene expression data has recently attracted a great deal of attention. One problem of interest is to relate genes to survival outcomes of patients with the purpose of building regression models for the prediction of future patients' survival based on their gene expression data. For this, several authors have discussed the use of the proportional hazards or Cox model after reducing the dimension of the gene expression data. This paper presents a new approach to conduct the Cox survival analysis of microarray gene expression data with the focus on models' predictive ability. The method modifies the correlation principal component regression (Sun, 1995) to handle the censoring problem of survival data. The results based on simulated data and a set of publicly available data on diffuse large B-cell lymphoma show that the proposed method works well in terms of models' robustness and predictive ability in comparison with some existing partial least squares approaches. Also, the new approach is simpler and easy to implement.


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