Alexandre Laterre

A multispecies foundation model for genome function prediction, gene annotation and sequence design

NTv3 is a foundation model that predicts genome function, annotates genes, and designs DNA sequences across species, outperforming existing models in multiple tasks.

Annotating the genome at single-nucleotide resolution with DNA foundation models

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.

AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks

AbBFN2 is a generative antibody foundation model that unifies diverse data sources and enables flexible conditional generation for multiple antibody design tasks.

Protein Sequence Modelling with Bayesian Flow Networks

This paper introduces ProtBFN, a Bayesian Flow Network for protein sequence generation, which outperforms existing models and is fine-tuned for antibody generation.

A Foundational Large Language Model for Edible Plant Genomes

This paper introduces a large language model for plant genomics that provides state-of-the-art predictions for regulatory annotations and variant prioritization, along with a benchmark dataset for deep learning methods.

ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein Tasks

ChatNT is a conversational agent that understands biological sequences and achieves state-of-the-art results on genomics tasks while being accessible to non-coders.

Early Computational Detection of Potential High Risk SARS-CoV-2 Variants

A COVID-19 Early Warning System combines structural modelling with machine learning to detect and monitor high risk SARS-CoV-2 variants, identifying all 16 WHO designated variants on average more than one and a half months in advance by selecting on average less than 0.3% of the weekly novel variants.