James W. Webber

The paper introduces a fast and accurate imputation method for miRNA expression data using constrained least squares, outperforming existing methods in speed and accuracy.

This study compares neural network architectures for multi-cancer classification using miRNA, protein, and mRNA data, finding that shallow networks outperform deeper models.

The paper introduces a sparse orthogonal projection method for dimensionality reduction, applied to miRNA expression analysis and cancer prediction, showing competitive classification performance.