Portrait of Can (Sam) Chen

Can (Sam) Chen

Collaborating Alumni - McGill University
Supervisor
Xue (Steve) Liu
Co-supervisor
Chris Pal
Research Topics
AI for Science
Diffusion Models
Large Language Models (LLM)
Multimodal Learning
Neural Scaling Laws
Reinforcement Learning
Website

Publications

MiRformer: a dual-transformer-encoder framework for predicting microRNA-mRNA interactions from paired sequences
Jiayao Gu
Can (Sam) Chen
Yue Li
MicroRNAs (miRNAs) are small non-coding RNAs that regulate genes by binding to target messenger RNAs (mRNAs), causing them to degrade or sup… (see more)pressing their translation. Accurate prediction of miRNA–mRNA interactions is crucial for RNA therapeutics. Existing methods rely on handcrafted features, struggle to scale to kilobase-long mRNA sequences, or lack interpretability. We introduce MiRformer , a transformer framework designed to predict not only the binary miRNA–mRNA interaction but also the start and end location of the miRNA binding site in the mRNA sequence. MiRformer employs a dual-transformer encoder architecture to learn interaction patterns directly from raw miRNA-mRNA sequence pairs via the cross-attention between the miRNA-encoder and mRNA-encoder. To scale to long mRNA sequences, we leverage sliding-window attention mechanism. MiR-former achieves state-of-the-art performance across diverse miRNA–mRNA tasks, including binding prediction, target-site localization, and cleavage-site identification from Degradome sequencing data. The learned transformer attention are highly interpretable and reveals highly contrasting signals for the miRNA seed regions in 500-nt long mRNA sequences. We used MiRformer to simultaneously predict novel binding sites and cleavage sites in 13k miRNA-mRNA pairs and observed that the two types of sites tend to be close to each other, supporting miRNA-mediated degradation mechanism. Our code is available at https://github.com/li-lab-mcgill/miRformer .
2025-11-23
bioRxiv (preprint)
doi.org