Maxence Gélard

CellScribe is a generative framework that unifies cell identity and state by using a language model to generate cell type annotations and meta-program profiles, outperforming baselines and identifying new cell types through in-context learning.

The paper introduces SegmentNT, a DNA foundation model for genome annotation, achieving state-of-the-art performance in identifying 14 genic and regulatory elements across species.

This paper introduces a bimodal model for bulk RNA-seq and DNA methylation that uses masked language modeling to learn representations, achieving state-of-the-art performance in cancer classification and survival prediction.

BulkRNABert uses a pre-trained transformer model on bulk RNA-seq data to improve cancer classification and survival prediction across multiple cancer types.