Ewan Birney on Muck Rack

Ewan Birney

Cambridge
As seen in: LinkedIn, Nature, The Guardian, Wiley Online Library, Frontiers, Cell Press, The Spectator, EMBL, Spectator US
Deputy Director General of EMBL, Director of EMBL-EBI. I have an insatiable love of biology. @ewanbirney@genomic.social. I also work with ONT, Dovetail + GeL.
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Articles

A panel of medaka isogenic lines suggests individual, seasonal, and sexual genetic variation of bdnf gene expression in the brain

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By Eleonora Rovegno, Christina Vasilopoulou, Saul Pierotti, Tomas Fitzgerald, Joachim Wittbrodt, Ewan Birney, Daniela Vallone, Felix Loosli, Cristiano Bertolucci, Nicholas Foulkes, Tyrone Lucon-Xiccato
| Frontiers
Abstract Introduction: Once recognized only in humans, variance in the cognitive phenotype is now acknowledged in a range of vertebrate species. However, our understanding of its underlying causes is still incomplete. Brain-derived neurotrophic factor (BDNF) is an essential protein for brain functioning and plays a key role in cognitive processes such as learning and memory, including interindividual variation.
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Author Correction: Learning the natural history of human disease with generative transformers - Nature

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By Artem Shmatko, Alexander Jung, Søren Brunak, Ewan Birney, Tom Fitzgerald
| Nature
Correction to: Nature https://doi.org/10.1038/s41586-025-09529-3 Published online 17 September 2025 In the version of this article initially published, in the Methods “Exponential waiting time model” section, there was a typographical error in the equation now reading “lossj = – log P(j) = cross_entropy(logits, tokens),” where a minus sign originally appeared before the cross-entropy term. The equation has been amended in the HTML and PDF versions of the article.
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Learning the natural history of human disease with generative transformers - Nature

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By Artem Shmatko, Alexander Jung, Søren Brunak, Ewan Birney, Tom Fitzgerald
| Nature
Abstract Decision-making in healthcare relies on understanding patients’ past and current health states to predict and, ultimately, change their future course1,2,3. Artificial intelligence (AI) methods promise to aid this task by learning patterns of disease progression from large corpora of health records4,5. However, their potential has not been fully investigated at scale.
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