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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

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.

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    Date Created
    • 2016-10-21
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  • Text
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    Identifier
    • Digital object identifier: 10.1111/eva.12417
    • Identifier Type
      International standard serial number
      Identifier Value
      1752-4563
    • Identifier Type
      International standard serial number
      Identifier Value
      1752-4571
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    Liu, L., Chang, Y., Yang, T., Noren, D. P., Long, B., Kornblau, S., . . . Ye, J. (2016). Evolution-informed modeling improves outcome prediction for cancers. Evolutionary Applications, 10(1), 68-76. doi:10.1111/eva.12417

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