Jiashi Feng

The paper introduces a framework called meta-matching to improve prediction performance in small datasets by leveraging correlations with large-scale phenotypic data.

This paper predicts Alzheimer's disease progression using a minimal recurrent neural network, showing better performance than baseline methods when handling missing data with model-based imputation.

This study compares deep neural networks and kernel regression in predicting behavior and demographics from resting-state functional connectivity data, finding comparable performance with kernel regression being more computationally efficient.