… predictive models of outcome for each connectivity measure, which were compared with
prediction … yielded a total of 783 connectivity features per subject. (B) Examples of the three …

… In general, most GCN studies focused on extracting useful features from brain connectivity
data to do classification or to conduct associative analysis. Inferring the relationship between …

… predictive modeling (38) using leave-one-subject-out cross-validation. In this pipeline,
feature-selection and model-… -based predictive modeling as our prediction approach because a …

… of the FC matrix as features for prediction, namely, each subject has a feature vector in the
dimension of (… To confirm that our predictive models captured FC variations specific to reading …

… More importantly, we found that M-module–based features achieve significantly higher
accuracy in predicting cancer stages (Supplementary Data). This result emphasizes the …

… We then constructed a linear regression predictive model with the 18 LASSO features
and the PANSS_total scores as the independent and dependent variables, respectively. To …

… lesion-deficit prediction models for each measure. For some measures, the FC prediction was
far … To further investigate shared features between models, we used multitask learning. All …

… Results from our comparative study suggest that functional connectivity (FC) measured with
DTW may … of machine learning models and feature engineering methods. As we investigated …

… By only doing matrix completion on selected features instead of all features, the computational
cost is greatly reduced and the focus benefits predictive modeling. RMC is applied on the …

… We trained a predictive model on pairs of resting-state and … models successfully learned
variations in both patient and control responses from the individual resting-connectivity features. …