Comparative Study on Microarray Data Analysis Techniques of Machine Lear ning

-Machine learning models has been applied for prediction and prognosis of disease development and it become an irrevocable part of microarray data analysis aimed at betterment of the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in cancer disease on the basis of clinical data is research work of the presented study. The paper discusses an approach to the problem in which the main factor used to predict survival time is the originally developed tumor-integrated clinical feature, which combines tumor stage, tumor size, and age at diagnosis. Two datasets from corresponding liver cancer studies are united by applying a data integration approach based on horizontal and vertical integration by using proper document-oriented and graph databases which show good performance and no data losses. Aside from data normalization and classification, the applied machine learning methods provide promising results in terms of accuracy of survival time prediction. Clustering has been used extensively as a vital tool of data mining. A neuro-fuzzy method is proposed in this research for analyzing the gene expression data from microarray experiments. Analysis of gene expression data leads to cancer identification and classification, which will facilitate proper treatment selection and drug development. The proposed approach was tested on three benchmark cancer gene expression data sets. Experimental results show that neuro-fuzzy method can be used as an efficient computational tool for microarray data analysis. The neuro-fuzzy classification system, which is based on a built clustering algorithm reached recognition rates than other classifiers. Gene expression data sets for liver cancer were analyzed in this research. A training and test data set for each cancer was used to analyze the quality of the genes. Use of computational methods to predict Gene Regulatory Networks (GRNs) from gene expression data is a challenging task. Many studies have been conducted using unsupervised methods to fulfill the task; however, such methods usually yield low prediction accuracies due to the lack of training data. In this research work, semi-supervised methods have been proposed for GRN prediction by utilizing two machine learning algorithms, namely support vector machines (SVM) and random forests (RF). The semi-supervised methods make use of unlabeled data for training. Inductive and transductive learning approaches has been investigated, both of which adopt an iterative procedure to obtain reliable negative training data from the unlabeled data. Then semi-supervised methods are applied to gene expression data of Escherichia coli and Saccharomyces cerevisiae, and evaluated the performance of the methods using the gene expression data. This analysis indicated that the transductive learning approach outperformed the inductive learning approach for both organisms. However, there was no conclusive difference identified in the performance of SVM and RF. Experimental results also showed that the proposed semi-supervised methods performed better than existing supervised methods for both organisms.