Two new computational tools apply machine learning to core molecular problems. ProteoBoostR debuted as an interactive supervised‑ML framework designed for clinical proteomics workflows, enabling researchers and clinicians to build and validate predictive models on proteomic datasets. The platform aims to streamline deployment of proteomic biomarkers into translational studies. Separately, HDGS‑Net, a hybrid dilated gated separable CNN, improved prediction of nucleosome occupancy, advancing chromatin‑level modeling. Both efforts underscore growing reliance on bespoke deep‑learning architectures to extract clinically relevant signals from high‑dimensional molecular data.