Sharon Aviran

This paper improves RNA secondary structure prediction by incorporating Markov models to capture context dependencies in SHAPE data, enhancing prediction accuracy.

This paper presents nearest neighbor parameters for estimating RNA folding stability with pseudouridine, derived from optical melting experiments, and demonstrates their application in modeling RNA secondary structures.

This paper develops new RNA folding nearest neighbor parameters for sequences containing 1-methyl-pseudouridine, showing it stabilizes RNA folding and improves tRNA ensemble modeling.

This paper evaluates deep learning methods for RNA secondary structure prediction, highlights generalization challenges, and discusses integration with traditional methods.

The paper introduces an improved method for detecting RNA stem-loops in structurome data, showing their association with RNA binding protein footprints.

The paper introduces a hairpin-centric approach to analyze RNA structure probing data, enabling rapid and accurate detection of local structural features and improving understanding of RNA function.

This paper introduces a Kalman filtering approach to improve the reliability of RNA SHAPE data by addressing noise and bias in reactivity measurements.