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dc.contributor.authorLiu, Li
Chang, Yung
Yang, Tao
Noren, David P.
Long, Byron
Kornblau, Steven
Qutub, Amina
Ye, Jieping
dc.date.accessioned 2016-12-16T17:50:26Z
dc.date.available 2016-12-16T17:50:26Z
dc.date.issued 2016
dc.identifier.urihttp://hdl.handle.net/1911/93725
dc.description.abstract Despite wide applications of high-throughput biotechnologies in cancer research, many biomarkers discovered by exploring large-scale omics data do not provide satisfactory performance when used to predict cancer treatment outcomes. This problem is partly due to the overlooking of functional implications of molecular markers. Here, we present a novel computational method that uses evolutionary conservation as prior knowledge to discover bona fide biomarkers. Evolutionary selection at the molecular level is nature's test on functional consequences of genetic elements. By prioritizing genes that show significant statistical association and high functional impact, our new method reduces the chances of including spurious markers in the predictive model. When applied to predicting therapeutic responses for patients with acute myeloid leukemia and to predicting metastasis for patients with prostate cancers, the new method gave rise to evolution-informed models that enjoyed low complexity and high accuracy. The identified genetic markers also have significant implications in tumor progression and embrace potential drug targets. Because evolutionary conservation can be estimated as a gene-specific, position-specific, or allele-specific parameter on the nucleotide level and on the protein level, this new method can be extended to apply to miscellaneous モomicsヤ data to accelerate biomarker discoveries.
dc.language.iso eng
dc.rights This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.title Evolution-informed modeling improves outcome prediction for cancers
dc.type Journal article
dc.citation.journalTitle Evolutionary Applications
dc.contributor.publisher Wiley
dc.type.dcmi Text
dc.identifier.doihttp://dx.doi.org/10.1111/eva.12417
dc.type.publication publisher version
dc.identifier.citation Liu, Li, Chang, Yung, Yang, Tao, et al., . "Evolution-informed modeling improves outcome prediction for cancers." Evolutionary Applications, (2016) http://dx.doi.org/10.1111/eva.12417.


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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Except where otherwise noted, this item's license is described as This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.