Marc Horlacher

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.

RIBEX improves RBP prediction by integrating sequence and interactome data, outperforming existing methods on IDR-rich and non-RBD proteins.

The paper benchmarks encoding methods for chemical, genetic, and cell line perturbations to predict cancer cell viability and gene dependency.

DeepLocRNA predicts RNA subcellular localization using transfer learning from pre-trained RBP-binding models, achieving state-of-the-art performance across human and mouse RNA.

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 ChromTransfer, a transfer learning method that improves cell-type specific chromatin accessibility prediction from DNA sequences using pre-trained models.

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.