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Research Papers

Sable: a Performant, Efficient and Scalable Sequence Model for MARL

Omayma Mahjoub | Sasha Abramowitz | Ruan de Kock | Wiem Khlifi | Simon du Toit | Jemma Daniel | Louay Ben Nessir | Claude Formanek | Louise Beyers | Liam Clark | Arnu Pretorius

ICML 2025 Jul 2025
Figure 1. Performance, memory, and scaling properties of Sable compared to the Multi-Agent Transformer (MAT) (Wen et al., 2022), the previous state-of-the-art, aggregated over 45 cooperative MARL tasks. Left: Sable ranks best in 34 out of 45 tasks, outperforming all other MARL algorithms tested across 6 environments: RWARE, LBF, MABrax, SMAX, Connector, and MPE. MAT ranked best of 3/45. Middle: Sable exhibits superior throughput, processing up to 6.5 times more steps per second compared to MAT as we scale to 512 agents. Right: Sable scales efficiently to thousands of agents, maintaining stable performance, while using GPU memory significantly more efficiently than MAT.
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Bimodal masked language modeling for bulk RNA-seq and DNA methylation representation learning

Maxence Gélard | Hakim Benkirane | Thomas Pierrot | Guillaume Richard | Paul-Henry Cournède

ICML 2025 Workshop Jul 2025
Figure 1: MOJO pipeline. (a) Each modality is first tokenized using linear binning. (b) MOJO, whose core architecture is composed of a mix of convolution and attention operations, is firstly pre-trained through bimodal masked language modeling. (c) Embeddings are probed from MOJO to fine-tune a task-specific head tailored for cancer-type classification or survival analysis.
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InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics

Jesper Lauridsen | Pathmanaban Ramasamy | Rachel Catzel | Vahap Canbay | Amandla Mabona | Kevin Eloff | Paul Fullwood | Jennifer Ferguson | Annekatrine Kirketerp-Møller | Ida Sofie Goldschmidt | Tine Claeys | Sam van Puyenbroeck | Santiago Nicolas Lopez Carranza | Erwin M. Schoof | Lennart Martens | Jeroen Van Goey | Chiara Frankavilla | Timothy Patrick Jenkins | Konstantinos Kalogeropoulos

Jul 2025
InstaNovo-P: A de novo peptide sequencing model for phosphoproteomics
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ChatNT: A Multimodal Conversational Agent for DNA, RNA and Protein Tasks

Bernardo P. de Almeida | Guillaume Richard | Hugo Dalla-Torre | Christopher Blum | Lorenz Hexemer | Priyanka Pandey | Stefan Laurent | Chandana Rajesh | Marie Lopez | Alexandre Laterre | Maren Lang | Uğur Şahin | Karim Beguir | Thomas Pierrot

Nature Machine Intelligence (2025) Jul 2025
ChatNT, the first multimodal conversational agent with an advanced understanding of biological sequences. ChatNT achieves new state-of-the-art results on the Nucleotide Transformer benchmark while being able to solve all tasks at once, in English, and to generalize to unseen questions.
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Unified framework for matchgate classical shadows

Valentin Heyraud | Héloise Chomet | Jules Tilly

npj Quantum Information Jul 2025
Unified framework for matchgate classical shadows
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Leveraging State Space Models in Long Range Genomics

Matvei Popov | Aymen Kallala | Anirudha Ramesh | Narimane Hennouni | Shivesh Khaitan | Rick Gentry | Alain-Sam Cohen

ICLR LMRL (2025) May 2025
Comparison of the extrapolation methods of state-space models and attention-based models on VEP eQTLs (AUROC). For NTv2, we also reported an inference-time extrapolation method: position interpolation. A dotted vertical line indicates the fine-tuning sequence length (12 kbp) of all models. Attention-based models collapse when processing sequences that are longer than what they have encountered at training time, whereas state-space models show an ability to generalize to sequences up to 10x longer. Lines that turn into dotted indicate values that we were unable to compute due to computational cost constraints and are therefore assumed based on trends.
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Open-Source and FAIR Research Software for Proteomics

Lukas Käll | Yasset Perez-Riverol | Wout Bittremieux | William S. Noble | Lennart Martens | Aivett Bilbao | Michael R. Lazear | Bjorn Grüning | Daniel S. Katz | Michael J. MacCoss | Chengxin Dai | Jimmy K. Eng | Robbin Bouwmeester | Michael R. Shortreed | Enrique Audain | Timo Sachsenberg | Jeroen Van Goey | Georg Wallmann | Bo Wen | William E. Fondrie

May 2025
Open-source software (OSS), aligned with the FAIR Principles (Findable, Accessible, Interoperable, Reusable), offers a solution by promoting transparency, reproducibility, and community-driven development, which fosters collaboration and continuous improvement. In this manuscript, we explore the role of OSS in computational proteomics, its alignment with FAIR principles, and its potential to address challenges related to licensing, distribution, and standardization.
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AbBFN2: A flexible antibody foundation model based on Bayesian Flow Networks

Bora Guloglu | Miguel Bragança | Alex Graves | Scott Cameron | Timothy Atkinson | Liviu Copoiu | Alexandre Laterre | Thomas D. Barrett

May 2025
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Metalic: Meta-Learning In-Context with Protein Language Models

Jacob Beck | Shikha Surana | Manus McAuliffe | Oliver Bent | Thomas D. Barrett | Juan Jose Garau Luis | Paul Duckworth

ICLR 2025 Apr 2025
Our method, called Metalic (Meta-Learning In-Context), uses in-context learning and fine-tuning, when data is available, to adapt to new tasks.
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Simple Guidance Mechanisms for Discrete Diffusion Models

Hugo Dalla-Torre | Sam Boshar | Bernardo P. de Almeida | Thomas Pierrot | Yair Schiff | Subham Sekhar Sahoo | Hao Phung | Guanghan Wang | Alexander Rush | Volodymyr Kuleshov

ICLR 2025 Apr 2025
Guidance mechanisms for discrete diffusion
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De novo peptide sequencing with InstaNovo: Accurate, database-free peptide identification for large scale proteomics experiments

Kevin Eloff | Konstantinos Kalogeropoulos | Oliver Morell | Amandla Mabona | Jakob Berg Jespersen | Wesley WIlliams | Sam P. B. van Beljouw | Marcin Skwark | Andreas Hougaard Laustsen | Stan J. J. Brouns | Stan J. J. Brouns | Erwin M. Schoof | Jeroen Van Goey | Ulrich auf dem Keller | Karim Beguir | Nicolas Lopez Carranza | Timothy P. Jenkins

Nature Machine Intelligence Mar 2025
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Bayesian Optimisation for Protein Sequence Design: Gaussian Processes with Zero-Shot Protein Language Model Prior Mean

Carolin Benjamins | Shikha Surana | Oliver Bent | Marius Lindauer | Paul Duckworth

NeurIPS 2024 workshop Dec 2024
Bayes Opt for Protein Design
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