J. Alcantara

This paper introduces a machine learning framework to identify and target transcriptomic vulnerabilities in colorectal cancer, demonstrating that differentiation therapy selectively eliminates cancer stem cells and reduces recurrence risk.

This study uses Gaussian accelerated molecular dynamics to simulate proline isomerization in proline-rich peptides, finding that cis conformations are frequently sampled and affect structural properties and binding potential.