Jaroslav Zendulka

TL;DR: This study constructed three independent datasets by removing all duplicities, inconsistencies and mutations previously used in the training of evaluated prediction tools, and returned results for all mutations, confirming that consensus prediction represents an accurate and robust alternative to the predictions delivered by individual tools.

TL;DR: A user-friendly web interface was developed that provides easy access to the five tools’ predictions, and their consensus scores, in a user-understandable format tailored to the specific features of different categories of variations.

TL;DR: A newly developed Bioconductor package for identifying potential quadruplex‐forming sequences (PQS), which allows for sequence searches that accommodate possible divergences from the optimal G4 base composition and demonstrates that the algorithm behind the searches has a 96% accuracy.

TL;DR: FireProt is a web server for the automated design of multiple-point thermostable mutant proteins that combines structural and evolutionary information in its calculation core and is complemented with interactive, easy-to-use interface that allows users to directly analyze and optionally modify designed thermostably mutants.

TL;DR: The concept of a "self-learner" that builds the machine learning models from the data generated during the current course, which utilises information about already submitted assessments, and introduces the problem of imbalanced data for training and testing the classification models.