Deep Learning in Alzheimer's disease: Diagnostic Classification and Prognostic Prediction using Neuroimaging Data

no code implementations • 2 May 2019 • Taeho Jo, Kwangsik Nho, Andrew J. Saykin

The combination of traditional machine learning for classification and stacked auto-encoder (SAE) for feature selection produced accuracies of up to 98. 8% for AD classification and 83. 7% for prediction of conversion from mild cognitive impairment (MCI), a prodromal stage of AD, to AD.

Classification feature selection +1

Evaluation of Protein Structural Models Using Random Forests

no code implementations • 13 Feb 2016 • Renzhi Cao, Taeho Jo, Jianlin Cheng

Our method uses both multi and single model quality assessment method for global quality assessment, and uses chemical, physical, geo-metrical features, and global quality score for local quality assessment.

Protein Structure Prediction