José Nunes de Alencar, Eduardo Ferreira Amorim, Matheus Kiszka Scheffer, Sandro Pinelli Felicioni, Mariana Fuziy Nogueira De Marchi
Poor evidence for poor R wave progression in coronary disease: A scoping review
Journal of electrocardiology
successful biomarker discovery essential develop computational frameworks summarize highdimensional neuroimaging data terms involved subsystems brain also revealing underlying heterogeneous functional structural changes covarying specific cognitive biological traits however unsupervised decompositions inculcate clinical assessment information supervised approaches extract individual feature importance thereby impeding qualitative interpretation level subspaces present novel framework extract robust multimodal brain subspaces associated changes given cognitive biological trait approach involves active subspace learning gradients trained machine learning model followed clustering extract summarize salient consistent subspaces associated target variable rigorous crossvalidation procedure alzheimers disease ad dataset framework successfully extracts multimodal subspaces specific given clinical assessment eg memory cognitive skills also retains predictive performance standard machine learning algorithms also show salient active subspace directions occur consistently across randomly subsampled repetitions analysis compared existing unsupervised decompositions based principle component analysis subspace components framework retain higher predictive information important step towards biomarker discovery framework uncovers adrelated brain regions associated brain subspaces also enables automated identification multiple underlying structural functional subsystems brain collectively characterize changes memory proficiency cognitive skills related brain disorders like ad