Bitbol, Anne-Florence

Protein language models trained on multiple sequence alignments learn phylogenetic relationships

Lupo, U., Sgarbossa, D., & Bitbol, A.-F. (2022) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 67

Protein language models trained on multiple sequence alignments learn phylogenetic relationships

Lupo, U., Sgarbossa, D., & Bitbol, A.-F. (2022) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 67

Generative power of a protein language model trained on multiple sequence alignments

Sgarbossa, D., Lupo, U., & Bitbol, A.-F. (2022) · eLife

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 43

Pairing interacting protein sequences using masked language modeling

Lupo, U., Sgarbossa, D., & Bitbol, A.-F. (2024) · Proceedings of the National Academy of Sciences

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 31

Pairing interacting protein sequences using masked language modeling

Lupo, U., Sgarbossa, D., & Bitbol, A.-F. (2023) · Proceedings of the National Academy of Sciences

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 31

ProtMamba: a homology-aware but alignment-free protein state space model

Sgarbossa, D., Malbranke, C., & Bitbol, A.-F. (2025) · Bioinformatics

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 27

Combining phylogeny and coevolution improves the inference of interaction partners among paralogous proteins

Gandarilla-Pérez, C. A., Pinilla, S., Bitbol, A.-F., & Weigt, M. (2023) · PLOS Computational Biology

Bioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

Cited by 13

Correlations from structure and phylogeny combine constructively in the inference of protein partners from sequences

Gerardos, A., Dietler, N., & Bitbol, A.-F. (2021) · PLoS Computational Biology

Protein Structure and DynamicsGenomics and Phylogenetic Studies

Cited by 12

Impact of phylogeny on structural contact inference from protein sequence data

Dietler, N., Lupo, U., & Bitbol, A.-F. (2023) · Journal of The Royal Society Interface

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 6

Impact of phylogeny on structural contact inference from protein sequence data

Dietler, N., Lupo, U., & Bitbol, A.-F. (2022) · Journal of The Royal Society Interface

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 6

RAG-ESM: Improving Pretrained Protein Language Models via Sequence Retrieval

Sgarbossa, D., & Bitbol, A.-F. (2025) · PRX Life

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 3

RAG-ESM: Improving pretrained protein language models via sequence retrieval

Sgarbossa, D., & Bitbol, A.-F. (2025) · PRX Life

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 3

ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa

Malbranke, C., Zalaffi, G. P., & Bitbol, A.-F. (2025) · Proceedings of the National Academy of Sciences

Bioinformatics and Genomic NetworksMachine Learning in Bioinformatics

Cited by 2

Impact of phylogeny on the inference of functional sectors from protein sequence data

Dietler, N., Abbara, A., Choudhury, S., & Bitbol, A.-F. (2024) · PLoS Computational Biology

Evolution and Genetic DynamicsGenomics and Phylogenetic Studies

Cited by 1

Impact of phylogeny on the inference of functional sectors from protein sequence data

Dietler, N., Abbara, A., Choudhury, S., & Bitbol, A.-F. (2024) · PLOS Computational Biology

Evolution and Genetic DynamicsGenomics and Phylogenetic Studies

Cited by 1

Out-of-Equilibrium Selection Pressure Enhances Inference from Protein Sequence Data

Dietler, N., Malbranke, C., & Bitbol, A.-F. (2026) · Physical Review Letters

Protein Structure and DynamicsEvolution and Genetic Dynamics

Cited by 1