Eloise L Flood, Lorene Schweig, Elizabeth B Froh, Warren D Frankenberger, Ruth M Lebet, Mei-Lin Chen-Lim, K Joy Payton, Margaret A McCabe
Arcus Experience: Bridging the Data Science Gap for Nurse Researchers
Nursing research
integrative cluster subtypes intclusts provide framework classification breast cancer tumors 10 distinct groups based copy number gene expression unique biological drivers disease clinical prognoses gene expression data often lacking accurate classification samples intclusts copy number data alone essential current classification methods achieve low accuracy gene expression data absent warranting development new approaches intclust classification copy number data 1980 breast cancer samples metabric used train multiclass xgboost machine learning algorithms copyclust piecewise constant fit applied average copy number profile intclust unique breakpoints across 10 profiles identified converted 500 genomic regions used features copyclust models consisted two approaches 10class model final intclust label predicted single multiclass model 6class model binary reclassification four pairs intclusts combined initial multiclass classification performance validated tcga dataset copy number data generated snp arrays wes platforms copyclust achieved 81 79 overall accuracy tcga snp wes datasets respectively ninepercentage point greater improvement overall intclust subtype classification accuracy copyclust achieves significant improvement current methods classification accuracy intclust subtypes samples without available gene expression data easily implementable algorithm intclust classification breast cancer samples copy number data