Identification of potential tissue-specific cancer biomarkers and development of cancer versus normal genomic classifiers.

ABSTRACT: Machine learning techniques for cancer prediction and biomarker discovery can hasten cancer detection and significantly improve prognosis. Recent "OMICS" studies which include a variety of cancer and normal tissue samples along with machine learning approaches have the potential to further accelerate such discovery. To demonstrate this potential, 2,175 gene expression samples from nine tissue types were obtained to identify gene sets whose expression is characteristic of each cancer class. Using random forests classification and ten-fold cross-validation, we developed nine single-tissue classifiers, two multi-tissue cancer-versus-normal classifiers, and one multi-tissue normal classifier. Given a sample of a specified tissue type, the single-tissue models classified samples as cancer or no

SUBMITTER: Mohammed A 

PROVIDER: S-EPMC5689641 | biostudies-literature | 2017 Oct

REPOSITORIES: biostudies-literature