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Beatriz Urda

@beatrizurda.bsky.social

138 followers 321 following 31 posts

PhDing at Barcelona Supercomputing Center | Exploring disease co-occurrences through omics, bioinformatics & HPC — with an eye on AI bias, and occasionally covered in clay.

Posts Replies Media

Beatriz Urda @beatrizurda.bsky.social · 02/09/2025

8/ Science already takes time, I hope to help make it worth it. Finally, terrified, we sent it to PNAS @pnas.org. After one round of review, reports came back: supportive. Positive. Accepted 🎉 🎉

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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025

1/ Everything that could go wrong in paper publishing… did. A story of patience, absurdity, and persistence 🌀 <1min From Alfonso Valencia’s lab and a very stubborn PhD student (me).

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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025

6/ Beyond known links, our approach points to potentially underdiagnosed comorbidities with strong molecular evidence and therapeutic implications. We made it all explorable here 👉 disease-perception.bsc.es/rgenexcom/

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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025

5/ We propose mechanistic models for disease co-occurrences 🔎 - Immune dysregulation was the most common thread - Specific genes & pathways highlight therapeutic implications - Even inverse comorbidities emerge (Huntington’s vs cancers, with opposite transcriptional programs)

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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025

4/ But patients aren’t homogeneous. So we stratified them into subgroups with similar gene expression profiles. This boosted recall to 64% with consistent precision. And crucially, it revealed numerous subgroup-specific comorbidities.

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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025

3/ We built a disease network from large-scale RNA-seq data on human diseases. 👉 It significantly captures ~50% of known comorbidities, far outperforming previous attempts.

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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025

🚨 New in PNAS! 🧬 64% of disease co-occurrences can be explained by transcriptomic similarities. Comorbidities aren’t random—they have a molecular basis. Here’s how we found it 👇 (1/n) 🔗 doi.org/10.1073/pnas.2421060122 @alfonsovalencia.bsky.social

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