Sobhan Shukueian Tabrizi

RPFace predicts RNA-protein interfaces directly from sequence, outperforming existing methods on RNA interfaces and generalizing to in vivo data without pretraining.

RPI-Zero predicts RNA-protein interactions without prior data by using a sequence-only model trained on a large benchmark of CLIP experiments.

RNA-X is a foundation model for designing RNA sequences that bind to multiple targets, including proteins, RNA, and DNA, and it outperforms existing methods in binding prediction and design.

This paper introduces a reinforcement learning-based method to dynamically protect genomic data sharing beacons against membership inference attacks by adapting strategies in real time.

RNAtranslator generates RNA sequences that bind to specific proteins by treating RNA design as a sequence-to-sequence translation task, producing novel and high-affinity sequences.