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Beatriz Urda

@beatrizurda.bsky.social
139 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.

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Reposted by Beatriz Urda
José Miguel Cacho @josemiguelcacho.bsky.social · 07/11/2025
Entrevista de Artur Olesch a @alfonsovalencia.bsky.social en #DigitalHealth sobre el incierto futuro de la #IA. Muy recomendable aboutdigitalhealth.com/2025/11/06/a...
aboutdigitalhealth.com
AI’s uncertain future
“If this is the final stage of AI, we’re in trouble.” — Alfonso Valencia on the risks and promise of generative models. Continue Reading
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Reposted by Beatriz Urda
BSC-CNS @bsc-cns.bsky.social · 17/10/2025
📄Ver publicación del primer estudio de comorbilidades en PNAS: pnas.org/doi/10.1073/... 🔘Plataforma interactiva de red de conexiones entre enfermedades: disease-perception.bsc.es/rgenexcom/ @beatrizurda.bsky.social @alfonsovalencia.bsky.social #DíaMundialCáncerMama #19Octubre
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BSC-CNS @bsc-cns.bsky.social · 17/10/2025
⭕ El BSC explora las conexiones entre enfermedades como el #cáncerdemama y busca distinguir qué parte de estas relaciones se explica por la #genética o por 𝗳𝗮𝗰𝘁𝗼𝗿𝗲𝘀 𝗮𝗺𝗯𝗶𝗲𝗻𝘁𝗮𝗹𝗲𝘀 o modificables. 🔘Casi la mitad de estas conexiones tiene un origen que 𝘃𝗮➕𝗮𝗹𝗹á 𝗱𝗲𝗹 𝗔𝗗𝗡 y son potencialmente modificables.
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Jack - amatica health @jackamatica.bsky.social · 10/09/2025
Some diseases show up together. Others rarely appear in the same person This study looked into whether gene activity (from RNA data) can help explain why The answer: yes - more than we thought Relevant as we are testing RNA in LC & ME/CFS patients @amaticahealth Breakdown:
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Reposted by Beatriz Urda
BSC-CNS @bsc-cns.bsky.social · 09/09/2025
💻🧬'Los vínculos secretos que existen entre enfermedades. El estudio del BSC representa el mayor esfuerzo hasta la fecha para explicar científicamente las asociaciones clínicas entre enfermedades' 🗞En @innovaspain.bsky.social ➡ www.bsc.es/4kD @alfonsovalencia.bsky.social @beatrizurda.bsky.social
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Beatriz Urda @beatrizurda.bsky.social · 08/09/2025
@growkudos.bsky.social @bsc-cns.bsky.social
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Beatriz Urda @beatrizurda.bsky.social · 08/09/2025
Excited to see our PNAS paper highlighted on Kudos, with an accessible take on the findings. Disease links are not random—they can be predicted from the expression of our genes. www.growkudos.com/publications... @pnas.org @alfonsovalencia.bsky.social 📄 doi.org/10.1073/pnas...
growkudos.com
Molecular map reveals hidden disease connections
Diseases rarely come alone. Many people experience a chain of diagnoses across their lives—for example, smoking-related lung cancer, asthma followed by Parkinson’s disease, or depression alongside lup...
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Reposted by Beatriz Urda
News Medical @newsmedical.bsky.social · 05/09/2025
🧬 New research in PNAS shows how gene expression patterns reveal why some diseases occur together while others don’t. Scientists uncovered hidden links — with immune pathways playing a major role. #GeneExpression #Comorbidity #PrecisionHealth 🔗 www.news-medical.net/news/2025090...
news-medical.net
Gene expression maps explain why diseases often occur together
This study reveals how gene expression patterns uncover molecular pathways linking comorbidities, enhancing treatment strategies for overlapping diseases.
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Beatriz Urda @beatrizurda.bsky.social · 04/09/2025
Thank you, Luís!!
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Reposted by Beatriz Urda
Luís M. Rocha @lmrocha.bsky.social · 03/09/2025
Great #networkmedicine work by @alfonsovalencia.bsky.social's team on deriving comorbidity networks from RNA-seq data to study complex disease relationships. Thy show that molecular mechanisms are behind many of the known comorbidities (often via immune response). doi.org/10.1073/pnas...
doi.org
Patient stratification reveals the molecular basis of disease co-occurrences | PNAS
Epidemiological evidence shows that some diseases tend to co-occur; more exactly, certain groups of patients with a given disease are at a higher r...
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Beatriz Urda @beatrizurda.bsky.social · 04/09/2025
Mil gracias 🤍🧬
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Beatriz Urda @beatrizurda.bsky.social · 03/09/2025
This was devastating.
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Reposted by Beatriz Urda
Beatriz Urda @beatrizurda.bsky.social · 03/09/2025
Totally! In our case it wasn’t even about sample size, but a basic textbook statistical concept 🤖. We even increased the sample size to show the results still held under the reviewer’s definition—and still, they wouldn’t budge.
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Beatriz Urda @beatrizurda.bsky.social · 03/09/2025
Totally! In our case it wasn’t even about sample size, but a basic textbook statistical concept 🤖. We even increased the sample size to show the results still held under the reviewer’s definition—and still, they wouldn’t budge.
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Beatriz Urda @beatrizurda.bsky.social · 03/09/2025
❤️
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Beatriz Urda @beatrizurda.bsky.social · 03/09/2025
Muchas gracias!
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
Muchas gracias por compartir!
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Reposted by Beatriz Urda
agenciasinc.es @agenciasinc.es · 02/09/2025
Un nuevo método computacional, creado en el @bsc-cns.bsky.social y basado en datos de más de 4 000 pacientes y 45 patologías, identifica conexiones clínicas conocidas y sugiere asociaciones inéditas con posibles aplicaciones en el diagnóstico y los tratamientos. www.agenciasinc.es/Noticias/El-...
agenciasinc.es
El BSC diseña un mapa molecular que descubre vínculos ocultos entre enfermedades
Un nuevo método computacional basado en datos de más de 4 000 pacientes y 45 patologías identifica conexiones clínicas conocidas y sugiere asociaciones inéditas con posibles aplicaciones en el diagnóstico y los tratamientos.
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Reposted by Beatriz Urda
Alfonso Valencia @alfonsovalencia.bsky.social · 02/09/2025
She explains part of the fight over 2 years with an absurd referee (can happen) and an incompetent profesional editor unable to understand even basic statistics - or worse unable to take a decision by him/her self. But never mind: Beatriz won and the paper is now published in a better journal.
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Alfonso Valencia @alfonsovalencia.bsky.social · 02/09/2025
Very happy to get it out. For scientific & personal reasons this one is special. Beatriz has done more work and endured the most difficult - and absurd - publications battles I can remember. Thanks to PNAS for being "normal" and congratulations to Beatriz. (Beatriz: next one will be easier!)
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
12/ This was the road. 👉 Here is the science: bsky.app/profile/beat... 📄 Paper: doi.org/10.1073/pnas.2421060122
doi.org
PNAS
Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
11/ Endless thanks to those who supported, listened, laughed, and advised: my colleagues, Davide Cirillo, and especially @alfonsovalencia.bsky.social, for his unwavering support throughout this wild journey. @bsc-cns.bsky.social
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
10/ And yes– I am officially fluent in rebuttals 🥋 It even helped me win Best Talk at ISMB/ECCB 2025 NetBio– for the science, the presentation, and (yes) the Q&A. #ISMBECCB2025
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
9/ I wouldn’t wish this road on anyone. But I’m proud we used the struggle to dig deeper– and that’s where we found some of the most interesting science. 🧬 Novel, underdiagnosed links & mechanisms with therapeutic potential 🧍Works even for rare diseases 🌐 A truly useful resource for the community
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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
7/ What I learned: Publishing can be arbitrary. Some reviewers make up their minds before seeing the evidence. One reviewer can wield disproportionate power. Rebuttals must be painfully clear. Editors often fail to step in, even when the situation is obvious. Don’t assume fairness in peer review 👇
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
6/ The results stood firm. The reviewer did not. It was tragic. And honestly, a bit comic.
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
5/ At one point, I was literally making diagrams with dogs 🐕 and dolphins 🐬 to explain a basic concept every colleague understood instantly. I even tested the reviewer’s hypothesis, which meant redoing everything with a dataset 3× bigger (yes, manually annotated).
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
4/ Publishing turned into a saga. ⏳ 4 years of my PhD ❌ 1 rejection after 3+ years 🔄 5 rounds of revisions 🤯 And an absurd fight with a single reviewer stuck on a single, simple statistical concept. By then, I was validating my patience, not the data.
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
3/ So I went all in. 📑 Months of manual curation 🧮 Developing methods from big omic data 🧬 Introducing patient stratification to uncover disease links And it worked. The signal was strong, robust, exciting. We thought “this will be hard work, but reasonably straightforward” Reader, it was not.
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
2/ It was 2019. I’d started working on a question that hooked me immediately: Why do diseases co-occur with each other? 👉 These patterns are everywhere in medicine, but their molecular basis was very elusive.
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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
In the end, comorbidities aren’t random, they can be predicted from the expression of our genes 🧬 And this is just the beginning. Stay tuned for what comes next 👀🚀
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
8/ In short: 🧬 Most comorbidities can be explained at the molecular level 🔎 Patient stratification reveals hidden & subtype-specific risks 💡 We propose new comorbidities with therapeutic implications 🌐 disease-perception.bsc.es/rgenexcom/
disease-perception.bsc.es
Patient stratification reveals the molecular basis of disease co-occurrences
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Beatriz Urda @beatrizurda.bsky.social · 02/09/2025
7/ Huge thanks to @alfonsovalencia.bsky.social for his continued support and excitement in this wild, fascinating project, and to all co-authors (Jon,Alba) & funding agencies. To the @bsc-cns.bsky.social for indispensable comput. resources, and to all patients & teams who made omic data available.
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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
2/ Many diseases co-occur more often than expected by chance 🎲 Studies such as those from @barabasi.bsky.social & @s_brunak mapped these clinical patterns beautifully. But the molecular drivers remained elusive. That’s what we set out to test.
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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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BSC-CNS @bsc-cns.bsky.social · 01/09/2025
🧬💻El BSC crea un método computacional que revela conexiones hasta ahora ocultas entre enfermedades. ℹMás info: www.bsc.es/4ke 💻 Acceder a la plataforma interactiva: disease-perception.bsc.es/rgenexcom/ 📄 Publicado en @pnas.org #supercomputación #IA #enfermedades
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NetBio COSI @netbio.bsky.social · 24/07/2025
👏🏆 Congrats to our #NetBio2025 prize winners: - Best Talk: Beatriz Urda-García - Best Poster Method: Arne Wehling - Best Poster Application: Sebastian Urquiza-Zurich - Best Poster Tool: Estefania Torrejon Thanks to all the (poster) presenters!!! It's amazing to have you in our community!!
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NetBio COSI @netbio.bsky.social · 22/07/2025
🎤 Next talk at #NetBio: @beatrizurda.bsky.social: Disentangling the genetic and non-genetic origin of disease co-occurrences #ISMBECCB2025 #NetworkBiology
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BSC-CNS @bsc-cns.bsky.social · 27/06/2025
🌐BSC is a proud partner of EU CIP project, that aims to create a #CancerInfoPortal to improve health literacy, empower patients, and reduce inequalities in access to cancer care information across Europe.
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Beatriz Urda @beatrizurda.bsky.social · 21/06/2025
I’m really curious: what else will we need to act? Enter your location and be moved. showyourstripes.info/c/europe/spa...
showyourstripes.info
Show Your Stripes
Visualising how the climate has changed for every country across the globe
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BSC-CNS @bsc-cns.bsky.social · 05/06/2025
🚬The molecular impact of cigarette smoking resembles aging across tissues ⭕This is the 1st comprehensive, multi-tissue analysis to show that smoking doesn’t just harm specific organs like the lungs 🫁 📄By BSC & Universidade do Porto researchers, published in Genome Medicine: bsc.es/Zt2
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Guillermo Prol-Castelo @gprolcastelo.bsky.social · 12/06/2025
New research alert 📝❗❗❗ In our latest pre-print, 2nd of my PhD, we performed a Systematic Literature Review on the use of Deep Representational Learning (DRL), especially the Variational Autoencoder (VAE), in cancer progression research. This thread explains our main findings. (1 minute read)
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