Ren Kikuchi, Toru Hirano, Kei Watanabe, Atsuki Sano, Tsuyoshi Sato, Takui Ito, Naoto Endo, Naohito Tanabe
Gender differences in the prevalence of low back pain associated with sports activities in children and adolescents: a six-year annual survey of a birth cohort in Niigata City, Japan
BMC musculoskeletal disorders
rna sequencing combined machine learning techniques provided modern approach molecular classification cancer class predictors reflecting disease class constructed known tissue types using gene expression measurements extracted cancer patients one challenge current cancer predictors often suboptimal performance estimates integrating molecular datasets generated different labs often quality data variable procured differently contains unwanted noise hampering ability predictive model extract useful information data preprocessing methods applied attempts reduce systematic variations harmonize datasets used build machine learning model resolving tissue origins aimed investigate impact data preprocessing stepsfocusing normalization batch effect correction data scalingthrough trial comparison goal improve crossstudy predictions tissue origin common cancers largescale rnaseq datasets derived thousands patients dozen tumor types results showed choice data preprocessing operations affected performance associated classifier models constructed tissue origin predictions cancer using tcga training set applying data preprocessing methods demonstrated batch effect correction improved performance measured weighted f1score resolving tissue origin independent gtex test dataset hand use data preprocessing operations worsened classification performance independent test dataset aggregated separate studies icgc geo therefore based findings publicly available largescale rnaseq datasets application data preprocessing techniques machine learning pipeline always appropriate