אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Regulation of mRNA translation, particularly under stress conditions, is a critical layer of gene expression control. Many of the elements that regulate translation during stress are embedded in the sequence of the mRNA, however, decoding how sequence regulates translation is still a largely unresolved question. Here we decided to harness the power of deep learning, in order to try and identify sequences that actively regulate translation, in particular, uORFs (upstream Open Readin Frames).

In this seminar, I will present our deep learning-based approach that utilizes experimental data from ribosome profiling (ribo-seq) experiments, to predict actively-regulating uORFs from different types. By augmentation of the training data using a new approach to generate a large synthetic database for pre-training, our model achieved improved classification performance of different uORF subtypes.

This approach demonstrates the potential of deep learning to advance our understanding of translation regulation.