Algorithmic fusion of gene expression profiling for diffuse large B-cell lymphoma outcome prediction

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
Qiuming ZhuWing C Chan

Abstract

Many different methods and techniques have been investigated for the processing and analysis of microarray gene expression profiling datasets. It is noted that the accuracy and reliability of the results are often dependent on the measurement approaches applied, and no single measurement so far is guaranteed to generate a satisfactory result. In this paper, an algorithmic fusion approach is presented for extracting genes that are predictive to clinical outcomes (survival-fatal) of diffuse large B-cell lymphoma on a set of microarray data for gene expression profiling. The approach integrates a set of measurements from different aspects in terms of the discrepancy indications and merit expectations of the gene expression patterns with respect to the clinical outcomes. A combination of statistical and non-statistical criteria, continuous and discrete parameterizations, as well as model-based and modeless evaluations is applied in the approach. By integrating these measurements, a set of genes that are indicative to the clinical outcomes are better captured from the gene expression profiling dataset.

References

Dec 3, 1999·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·A Ben-DorZ Yakhini
Jan 5, 2000·Proceedings of the National Academy of Sciences of the United States of America·M P BrownD Haussler
Jun 21, 2002·The New England Journal of Medicine·Andreas RosenwaldUNKNOWN Lymphoma/Leukemia Molecular Profiling Project

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Citations

Dec 13, 2012·IEEE Reviews in Biomedical Engineering·Oliver FaustToshiyo Tamura
Apr 17, 2009·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·Chun-Hou ZhengXiang-Zhen Kong

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