Abstract
Cancer is a disease driven by pathway activity, while useful biomarkers to predict outcome (prognostic markers) or determine treatment (treatment markers) rely on individual genes, proteins, or metabolites. We provide a novel approach that isolates pathways of interest by integrating outlier analysis and gene set analysis and couple it to the top-scoring pair algorithm to identify robust biomarkers. We demonstrate this methodology on pediatric acute myeloid leukemia (AML) data. We develop a biomarker in primary AML tumors, demonstrate robustness with an independent primary tumor data set, and show that the identified biomarkers also function well in relapsed AML tumors.
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Keywords
- Acute Myeloid Leukemia
- Outlier Analysis
- Diagnostic Sample
- Pediatric Acute Myeloid Leukemia
- Relapse Sample
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Ochs, M.F., Farrar, J.E., Considine, M., Wei, Y., Meschinchi, S., Arceci, R.J. (2013). Outlier Gene Set Analysis Combined with Top Scoring Pair Provides Robust Biomarkers of Pathway Activity. In: Ngom, A., Formenti, E., Hao, JK., Zhao, XM., van Laarhoven, T. (eds) Pattern Recognition in Bioinformatics. PRIB 2013. Lecture Notes in Computer Science(), vol 7986. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39159-0_5
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DOI: https://doi.org/10.1007/978-3-642-39159-0_5
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