Oncogenes and pathway identification using filter-based approaches between various carcinoma types in lung.
Lung cancer accounts for the most cancer-related deaths. The identification of cancer-associated genes and the related pathways are essential to prevent many types of cancer. In this paper, a more systematic approach is considered. First, we did pathway analysis using Hyper Geometric Distribution (HGD) and significantly overrepresented sets of reactions were identified. Second, feature-selection-based Particle Swarm Optimisation (PSO), Information Gain (IG) and the Biomarker Identifier (BMI) for the identification of different types of lung cancer were used. We also evaluated PSO and developed a new method to determine the BMI thresholds to prioritize genes. We were able to identify sets of key genes that can be found in several pathways. Experimental results show that our method simplifies features effectively and obtains higher classification accuracy than the other methods from the literature.
Visvanathan
Mahesh
M
Bioinformatics Core Facility, University of Kansas Lawrence, KS 66047, USA. mvisvanathan@ku.edu
Netzer
Michael
M
Seger
Michael
M
Adagarla
Bhargav S
BS
Baumgartner
Christian
C
Sittampalam
Sitta
S
Lushington
Gerald H
GH
D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics*; D053858:Metabolic Networks and Pathways; D009857:Oncogenes; D013269:Stochastic Processes