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    Evolution-informed modeling improves outcome prediction for cancers

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    Author
    Liu, Li; Chang, Yung; Yang, Tao; Noren, David P.; Long, Byron; More... Kornblau, Steven; Qutub, Amina; Ye, Jieping Less...
    Date
    2016
    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.
    Citation
    Liu, Li, Chang, Yung, Yang, Tao, et al.. "Evolution-informed modeling improves outcome prediction for cancers." Evolutionary Applications, (2016) Wiley: http://dx.doi.org/10.1111/eva.12417.
    Published Version
    http://dx.doi.org/10.1111/eva.12417
    Type
    Journal article
    Publisher
    Wiley
    Citable link to this page
    https://hdl.handle.net/1911/93725
    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.
    Link to License
    https://creativecommons.org/licenses/by/4.0/
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    • Bioengineering Publications [632]
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    Home | FAQ | Contact Us | Privacy Notice | Accessibility Statement
    Managed by the Digital Scholarship Services at Fondren Library, Rice University
    Physical Address: 6100 Main Street, Houston, Texas 77005
    Mailing Address: MS-44, P.O.BOX 1892, Houston, Texas 77251-1892
    Site Map