arXiv:2512.08613v1 Announce Type: new
Abstract: Predicting protein secondary structures such as alpha helices, beta sheets, and coils from amino acid sequences is essential for understanding protein function. This work presents a transformer-based model that applies attention mechanisms to protein sequence data to predict structural motifs. A sliding-window data augmentation technique is used on the CB513 dataset to expand the training samples. The transformer shows strong ability to generalize across variable-length sequences while effectively capturing both local and long-range residue interactions.
Randomized controlled trial to evaluate an app-based multimodal digital intervention for people with type 2 diabetes in comparison to a placebo app
IntroductionThis multi-center, parallel-group randomized controlled trial evaluated the app-based intervention mebix, developed by Vision2b GmbH in Germany, for people with type 2 diabetes compared to