Thomas Barrett
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Protein sequence modelling with Bayesian flow networks - Nature Communications
Abstract Exploring the vast and largely uncharted territory of amino acid sequences is crucial for understanding complex protein functions and the engineering of novel therapeutic proteins. Whilst generative machine learning has advanced protein sequence modelling, no existing approach is proficient in both unconditional and conditional generation. In this work, we propose that Bayesian Flow Networks (BFNs), a recently introduced framework for generative modelling, can address these challenges.
"Single-Cell Multi-Omic Analysis of the Vestibular Schwannoma Ecosystem" by Thomas F Barrett, Bhuvic Patel et al.
Keywords Humans, Neuroma, Acoustic, Ecosystem, Multiomics, Schwann Cells, Signal Transduction, Single-Cell Analysis, Tumor Microenvironment Abstract Vestibular schwannomas (VS) are benign tumors that lead to significant neurologic and otologic morbidity. How VS heterogeneity and the tumor microenvironment (TME) contribute to VS pathogenesis remains poorly understood. In this study, we perform scRNA-seq on 15 VS, with paired scATAC-seq (n = 6) and exome sequencing (n = 12).
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