Mutational signatures for breast cancer diagnosis using artificial intelligence.
Patrick OdhiamboHarrison OkelloAnnette WakaanyaClabe WekesaPatrick OkothPublished in: Journal of the Egyptian National Cancer Institute (2023)
Specific DNA-maintenance defects, endogenous or environmental exposures, and cancer genomic signatures are connected. The PubMed database (Geneshot) search for the keywords yielded a total of 21,921 genes associated with breast cancer. Then, based on their propensity to result in gene mutations, the genes were screened using the Phenolyzer software. These platforms lend credence to the fact that breast cancer diagnosis using Cytoscape 3.9.1, Phenolyzer, and Geneshot 2021 reveals high profile of the following mutational signatures: BRCA1, BRCA2, TP53, CHEK2, PTEN, CDH1, BRIP1, RAD51C, CASP3, CREBBP, and SMAD3.
Keyphrases
- artificial intelligence
- genome wide
- breast cancer risk
- machine learning
- deep learning
- big data
- papillary thyroid
- epithelial mesenchymal transition
- emergency department
- risk assessment
- cell proliferation
- cell free
- dna damage
- oxidative stress
- air pollution
- gene expression
- copy number
- squamous cell
- data analysis
- circulating tumor
- bioinformatics analysis
- signaling pathway