Lambert Moyon

Parnet is a CLIP-seq-based foundation model for RNA sequence representation learning that outperforms existing methods in binding profile prediction and generalizes across tasks.

The paper identifies 121 lncRNA driver mutations across 19 cancer types using 12,631 genomes, showing mutations disrupt RNA-binding protein interactions to promote oncogenesis.

The paper constructs co-expression networks using gene and isoform data from individuals with and without stress-related psychiatric disorders, revealing distinct network structures and regulatory rewiring.

The paper shows that microRNA activity is essential for early mouse embryos to transition into diapause, with miRNAs regulating gene expression and linking mTOR activity to miRNA biogenesis.

This study benchmarks 11 machine learning methods for predicting in vivo RBP-RNA interactions across hundreds of CLIP-seq datasets and RBPs.

RBPNet predicts CLIP crosslink count distributions from RNA sequences, outperforming existing methods and enabling variant impact scoring.

This paper introduces FINSURF, a machine learning tool that improves the prediction of pathogenic non-coding variants, achieving high accuracy in identifying disease-causing variants in clinical settings.

The paper maps human RBP-viral RNA interactions for SARS-CoV-2 and related coronaviruses using deep learning, identifies variant impacts, and links RBPs to potential biomarkers.

The paper identifies rare CRE variants associated with X-linked intellectual disability by combining sequencing, functional assays in zebrafish and mice, and phenotypic analysis.