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Journal of Computer Science & Systems Biology | ISSN: 0974-7230 | Volume: 11
&
Biostatistics and Bioinformatics
Big Data Analytics & Data Mining
7
th
International Conference on
7
th
International Conference on
September 26-27, 2018 | Chicago, USA
Feature selections in microarray survival data analysis using Boruta algorithm
Kazeem Adesina Dauda
Kwara State University, Nigeria
W
ith the existence of microarray data in the bioinformatics and clinical areas, the following questions frequently arise for both
computer and biological scientists that which genes among the thousands of genes are significantly involved in classifying
cancer classes and which genes are statistically significant with respect to specific cancer pathology. This study focuses on microarray
survival data where the number of covariates is greatly exceeding the number of observations. Series of analytical methodological
models have been developed to identify and classify informative genes from the gene expression and microarray survival data;
however, the integrity of the reported genes is still uncertain. In this regard, this study is motivated to propose a hybridized model with
respect to Boruta Algorithms (BAs) and Cox Proportional Hazard model (Cox-PH), to extract and identify the highly differentially
expressed genes for specific cancer pathology. A real-life data on blood cancer (lymphoma) was considered and the proposed method
together with Iterative Bayesian Model Averaging (IBMA) was used to select highly expressed genes from the data. The performance
of the two algorithms was tested based on the number of genes selected and the system time and it was observed that the proposed
algorithm perform better than the IBMA algorithm irrespective of the criteria.
kazeem.dauda@kwasu.edu.ngJ Comput Sci Syst Biol 2018, Volume: 11
DOI: 10.4172/0974-7230-C1-021