A Workbench using Evolutionary Genetic Algorithms for analyzing association in TCGA Data

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    Modern methods of acquiring molecular data have improved rapidly in recent years, making it easier for researchers to collect large volumes of information. However, this has increased the challenge of recognizing interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in the cancer genome atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be searched using an evolutionary algorithm approach. To showcase ACE, we assessed which RNA-sequencing transcripts were associated with estrogen receptor (ESR1) in the TCGA breast cancer cohort. The analysis revealed already well-established associations with XBP1 and FOXA1, but also identified a strong association with CT62, a potential immunotherapeutic target with few previous associations with breast cancer. In conclusion, ACE can produce results for very large searches in a short time and will serve as an increasingly useful tool for biomarker discovery in the big data era.

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    • ACE: A Workbench using Evolutionary Genetic Algorithms for analyzing association in TCGA Data

      Rights statement: Copyright 2019 American Association for Cancer Research. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.

      Accepted author manuscript, 581 KB, PDF-document

      Embargo ends: 13/02/2020

    DOI

    Original languageEnglish
    JournalCancer Research
    Journal publication date13 Feb 2019
    Early online date13 Feb 2019
    DOIs
    Publication statusEarly online date - 13 Feb 2019

    ID: 164752679