Filter-wrapper combination and embedded feature selection for gene expression data

Biomedical and bioinformatics datasets are generally large in terms of their number of features - and include redundant and irrelevant features, which affect the effectiveness and efficiency of classification of these datasets. Several different features selection methods have been utilised in various fields, including bioinformatics, to reduce the number of features. This study utilised Filter-Wrapper combination and embedded (LASSO) feature selection methods on both high and low dimensional datasets before classification was performed. The results illustrate that the combination of filter and wrapper feature selection to create a hybrid form of feature selection provides better performance than using filter only. In addition, LASSO performed better on high dimensional data

A Novel computer assisted genomic test method to detect breast cancer in reduced cost and time using ensemble technique

Breast cancer is the leading cause of death among women around the world. It is a primary malignancy for which genetic markers have revealed the ability for clinical decision making. It is a genetic disease that generates due to gene mutations, but the cost of a genetic test is relatively high for a number of patients in developing nations like India. The results of a genetic test can take a few weeks to determine cancer. This time duration influences the prognosis of genes since certain patients suffer from a high rate of malignant cell proliferation. Therefore, a computer-assisted genetic test method (CAGT) is proposed to detect breast cancer. This test method will predict the gene expressions and convert these expressions in the state of mutation (under-expression (-1), transition (0) overexpression (1)) and afterwards perform the classification to get the benign and malignant class in reduced time and cost. In the research work, machine learning techniques are applied to identify the most responsive genes of breast cancer on the premises of the clinical report of a patient and generated a CAGT. In the research work, the hard voting ensemble approach is applied to detect breast cancer on the basis of most responsive genes by CAGT which leads to improving 3.5% accuracy in cancer classification