Twiner: correlation-based regularization for identifying common cancer gene signatures.
Marta B LopesSandra CasimiroSusana VingaPublished in: BMC bioinformatics (2019)
Our analysis led to the identification of genes that show a similar correlation pattern in BRCA and PRAD transcriptomic data, and are selected as key players in the classification of breast and prostate samples into ER+ BRCA/AR+ TNBC/PRAD tumor and normal tissues, and also associated with survival time distributions. The results obtained are supported by the literature and are expected to unveil the similarities between the diseases, disclose common disease biomarkers, and help in the definition of new strategies for more effective therapies.
Keyphrases
- genome wide
- prostate cancer
- genome wide identification
- bioinformatics analysis
- papillary thyroid
- systematic review
- deep learning
- copy number
- dna methylation
- gene expression
- breast cancer risk
- single cell
- big data
- rna seq
- breast cancer cells
- estrogen receptor
- benign prostatic hyperplasia
- lymph node metastasis
- endoplasmic reticulum
- transcription factor
- young adults
- data analysis
- childhood cancer
- free survival