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. 2009;2(3):236-51.
doi: 10.1504/IJCBDD.2009.030115. Epub 2009 Dec 10.

Oncogenes and pathway identification using filter-based approaches between various carcinoma types in lung

Mahesh Visvanathan  1 , Michael Netzer, Michael Seger, Bhargav S Adagarla, Christian Baumgartner, Sitta Sittampalam, Gerald H Lushington
Affiliations

Oncogenes and pathway identification using filter-based approaches between various carcinoma types in lung

Mahesh Visvanathan et al. Int J Comput Biol Drug Des. 2009.

Abstract

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.

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Figures

Figure 1
Figure 1
Histogram of calculated BMI scores (schematic illustration). Grey areas indicate BMI scores of genes with good or excellent discrimination where the black area in the middle represents BMI values of genes with weak or no discrimination
Figure 2
Figure 2
(a) Identified clusters on the BMI scores using the k-means algorithm for adenocarcinoma vs. small-cell lung cancer and (b) the related histogram plot. Green: primary genes; black: secondary genes; blue: tertiary genes. In the left figure (a) the absolute BMI scores are displayed according to their sorted rank (index) (see online version for colours)
Figure 3
Figure 3
(a) Identified clusters on IG scores using the k-means clustering algorithm for adenocarcinoma vs. small-cell carcinoma (red: primary genes; green: secondary genes; blue: tertiary genes) and (b) the related histogram plot (see online version for colours)
Figure 4
Figure 4
Histogram plot of BMI scores for comparing squamous-cell vs. adenocarcinoma indicating a higher ratio of under-expressed genes (BMI-scores < 0)
See this image and copyright information in PMC

Comment in

  • Findings of research misconduct.
    [No authors listed] [No authors listed] NIH Guide Grants Contracts (Bethesda). 2012 Jan 6:NOT-OD-12-030. NIH Guide Grants Contracts (Bethesda). 2012. PMID: 22242231 Free PMC article. No abstract available.

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