Sign in
BioinformaticsCancer Research
Peer-reviewed journal article

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

Alan R. Gilmore, Matthew AlderdiceiD, Kienan I. SavageiD, Paul G. O’ReillyiD, Aideen C. RoddyiD, Philip D. DunneiD, Mark LawleriD, Simon S. McDadeiD, David J J WaughiD, Darragh G. McArtiD

Abstract

Modern methods in generating molecular data have dramatically scaled in recent years, allowing researchers to efficiently acquire large volumes of information. However, this has increased the challenge of recognising 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 selected for searching, and highlights significant correlations within the chosen data. It is based on an evolutionary algorithm which is capable of producing results for very large searches in a short time.

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.