What is it about?
Lung cancer commonly returns after years of treatment. Therefore, it is of paramount importance to predict Lung cancerrecurrence so that specific treatments can be sought. Nonetheless, conventional methods of predicting cancer recurrence rely solely on histopathology and the results are not very reliable. The microarray gene expression technology is a promising technology that could be used to predict Lung cancer recurrence by analyzing the gene expression of sample cells. This paper proposes a new model to use microarray datasets for Lung cancer recurrence prediction.
Featured Image
Why is it important?
This paper proposes a new effective GEP model on microarray datasets to predict the Lung cancer recurrence. This model, with other proposed methods, is able to select informative genes of Lung cancer for constructing an effective prediction model which can achieve a higher accuracy.
Perspectives
The proposed model present promising results and that encourage us for further research.Therefore, how to more effectively select informative genes for the GEP model to get higher efficiency and effectiveness is one of our research directions in the near future. Another research direction is to apply the GEP model to integrate datasets that contain lung cancer related data, such as clinical, imaging and genomic data, and do more experiments to exam the generality of the GEP model in predicting lung cancer or other cancer recurrence.
Mrs Russul Alanni
Deakin University
Read the Original
This page is a summary of: Prediction of Non-Small Cell Lung Cancer Recurrence from Microarray Data with Gene Expression Programming , IET Systems Biology, January 2017, the Institution of Engineering and Technology (the IET),
DOI: 10.1049/iet-syb.2016.0033.
You can read the full text:
Contributors
The following have contributed to this page
