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

Alan Gilmore, Matthew Alderdice, Kienan Savage, Paul G. O’Reilly, Aideen Roddy, Philip Dunne, Mark Lawler, Simon McDade, David Waugh, Darragh McArt

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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.
Original languageEnglish
Pages (from-to)2072
JournalCancer Research
Issue number8
Early online date13 Feb 2019
Publication statusEarly online date - 13 Feb 2019


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