Abstract:
Novel diagnostic tools promise the development of patient-tailored cancer treatment. However, one major step towards individualized therapy is to use a combination of various data sources, e.g. transcriptomic, proteomic, and clinical data. We have integrated clinical data and lung cancer microarray data that were generated on two different oligonucleotide platforms. We were interested in the question whether the prediction of survival outcome benefits from the integration of clinical and transcriptomic data. In addition, we attempted to identify those genes whose expression profiles correlate with survival outcome. We applied five machine learning techniques to predict survival risk groups, and we compared the models with respect to their performance and general user acceptance. Based on quantitative and qualitative evaluation criteria, we chose decision trees as the most relevant technique for this type of analysis. Our in silico analysis corroborates the role of numerous marker genes already described in lung adenocarcinomas. In addition, our study reveals a set of highly interesting genes whose expression profiles correlate with genetic risk groups of unexpected survival outcomes.
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Berrar, D., Sturgeon, B., Bradbury, I., Downes, C.S., Dubitzky, W. (2005). Integration of Microarray Data for a Comparative Study of Classifiers and Identification of Marker Genes. In: Shoemaker, J.S., Lin, S.M. (eds) Methods of Microarray Data Analysis. Springer, Boston, MA. https://doi.org/10.1007/0-387-23077-7_12
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DOI: https://doi.org/10.1007/0-387-23077-7_12
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