Bioenergetic and vascular predictors of potential super-ager and cognitive decline trajectories—a UK Biobank Random Forest classification study

A machine learning approach for potential Super‐Agers identification using neuronal functional connectivity networks

“…We previously found that vascular and metabolic blood biomarkers achieved good performance in distinguishing between UK Biobank participants showing cognitive gains or decline over 7 to 10 years. 12 The current study focused on whether the neuronal functional connectivity network, an indirect measure of neural activity, successfully predicted different cognitive trajectory types over time in UK biobank adults. We propose a novel hybrid algorithm, OLBO, that incorporates machine learning and BO to distinguish between Positive‐Agers and Cognitive Decliners using demographics and rsfMRI data.…”

“…We previously found that vascular and metabolic blood biomarkers achieved good performance in distinguishing between UK Biobank participants showing cognitive gains or decline over 7 to 10 years. 12 The current study focused on whether the neuronal functional connectivity resting state connectivity. 32 Interestingly, higher posterior DMN connectivity was the strongest network predictor for being a Positive-Ager.…”

“… 8 , 9 , 10 , 11 Similarly, we have recently found that roughly 20% of middle‐aged to early aged adults in the UK Biobank show gains over time in fluid intelligence, a form of executive function strongly impacted by aging. 12 Cognitive health is considered an important factor in successful aging. 13 Therefore, it is necessary to investigate the neural correlates of Positive‐Agers, adults in midlife with cognitive gain over time, to determine how brain function may differ compared to cognitively unimpaired adults who nonetheless show age‐related cognitive decline.…”

Anatomically resolved transcriptome and proteome landscapes reveal disease‐relevant molecular signatures and systematic changes in heart function of end‐stage dilated cardiomyopathy

“…The area under the ROC curve (AUC) of the proposed protein panel was used as a metric to evaluate the sensitivity and specificity of the biomarker performance. A random forest (RF) model is one of the most‐used supervised machine learning algorithm which is applied to predict disease without hyper‐parameter tuning 43–46 . The accuracy of the RF model at predicting DCM was tested using least absolute shrinkage and selection operator (LASSO) regression.…”

“…A random forest (RF) model is one of the most-used supervised machine learning algorithm which is applied to predict disease without hyper-parameter tuning. [43][44][45][46] The accuracy of the RF model at predicting DCM was tested using least absolute shrinkage and selection operator (LASSO) regression. There are several other variable selection methods that have traditionally been used to build models, however, the LASSO is based on minimizing mean squared error, which is based on balancing the opposing factors of bias and variance to build the most predictive model and minimize prediction error.…”