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Oncogene Identification using Filter based Approaches between Various Cancer Types in Lung

Authors: Michael Netzer, Bernhard Pfeifer, Christian Baumgartner, Michael Seger, Mahesh Visvanathan, Gerald H. Lushington

Abstract:

Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.

Keywords: Data Mining, Lung cancer, Feature selection, micro arrays

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055373

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