ACE: A Workbench Using Evolutionary Genetic Algorithms for Analyzing Association in TCGA.
Alan R GilmoreMatthew AlderdiceKienan I SavagePaul G O'ReillyAideen C RoddyPhilip David DunneMark LawlerSimon S McDadeDavid J WaughDarragh G McArtPublished in: Cancer research (2019)
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. SIGNIFICANCE: ACE uses an evolutionary algorithm approach to perform large searches for associations between any combinations of data in the TCGA database.
Keyphrases
- big data
- machine learning
- estrogen receptor
- angiotensin converting enzyme
- artificial intelligence
- genome wide
- single cell
- angiotensin ii
- electronic health record
- deep learning
- computed tomography
- gene expression
- emergency department
- papillary thyroid
- dna methylation
- copy number
- high throughput
- childhood cancer
- human health
- image quality
- single molecule
- breast cancer risk
- water quality