
🧬 Genetics & ML geek 🤖 PI @GenomeDataLab studying mutations, epigenet, mRNA, cancer, gene regul, AI for sci. Prof 🇩🇰 @UCPH_Research, 🇪🇸 GL @IRBBarcelona
Copenhagen/Barcelona
Joined July 2009
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Fran Supek retweeted
Can we cure most human diseases in the next 5–10 years? Absolutely not.
Is it still the right thing to aim at and invest in? Absolutely yes
Replying to @DarioAmodei
2/2 Second, on the messaging around AI. I do not agree that my messaging has been disproportionately negative. In fact it has been about equally balanced between risks and benefits: I’ve written one major essay about each, and even in interviews where I discuss the risks, I make sure to frequently mention the incredible benefits as well as proposing possible solutions to the risks (short clips from my interviews that end up on social media tend to be disproportionately negative, as that gets clicks). In fact, I wrote Machines of Loving Grace because I didn’t feel the AI industry was painting an inspiring enough picture of how the technology could radically transform the world for the better. The bulk of the essay is devoted to refuting skepticism of AI’s potential in health and biology, and showing why I think it will actually be possible to cure most human disease in ~5-10 years, as crazy as it may sound to ordinary people and frankly to biologists as well (I used to be one!). And, if you read my most recent essay (Policy on the AI Exponential), I discuss concrete proposals for how to streamline the FDA process to make sure the deluge of AI-accelerated drugs isn’t slowed down by the regulatory process. I feel the urgency here: I lost my father to Hepatitis C only a few years before the development of direct-acting antivirals (sofosbuvir), which cure 95% of patients and probably would have cured him.
I do agree that the public has a negative view of AI (and that this is a big problem), but I don’t think it is primarily caused by me or any other AI leader warning about AI’s risks. I think it is fundamentally a crisis of trust. I think that ordinary people don’t trust companies, governments, or the tech industry and always suspect that we are cooking up some new way to screw them over. The causes of this go back decades and AI is just the latest iteration of it. I don’t think that a glitzy marketing campaign with a positive spin (which some have advocated that Anthropic do) is the way to win back that trust — at this point, saying that AI will cure cancer is more a cliche than it is inspiring, and most people think it is deceptive. The thing that will work is *actually curing cancer*. I think by far the most accurate criticism of AI companies including Anthropic is that we haven’t yet delivered on our big promises to benefit the world. That is totally on us, and I think it’s the criticism you should be making, instead of all this stuff about messaging and marketing.
We are however doing our best to fix this: Anthropic is ramping up its efforts very quickly in biology and medicine, and we hope to have incredible results in the coming years and some early glimmers in the coming months. When we’ve actually accomplished something real, the whole world will hear about it, as loudly as possible, you have my word on that. But until then I don’t want to make empty promises, and in the meantime I feel compelled to speak honestly about the very real risks of AI and how to address them. Honesty is the right thing on the merits, and in terms of public credibility and trust it is no worse than, and may in fact be better than, an approach that ignores or distracts from risks which people instinctively understand are real.
Hot off the press – new study from the group:
🧬🤖 A DNA language model for predicting mutation risk trained on massive genomic data sets
A mutation seen again and again across cancer genomes looks like a causal change. But sometimes the DNA site is simply very easy to mutate and so generates a hotspot 🔥.
We built MutFormer AI model to separate these two possibilities: operating at single-nucleotide resolution, it separates “driver” from “passenger” hotspots.
(awesome work by Ivan Galvan 🔝, Marcell Veiner @MarcellVeiner et al. 👏)
🤖 Made with AI
Very nice summary of novelty/impact of our study by the @qedScience AI reviewbot, have a look 👇
Read preprint on bioRxiv: Transformer models of mutation risk at base-pair resolution identify non-coding hotspot cancer driver mutations
biorxiv.org/content/10.64898…
Bravo @GenomeDataLab team! 👏👏Congrats to Ivan Galvan & Marcell Veiner, w/ Dani Naro 🔝
DNA mismatch repair (MMR) deficiency is an important driver of cancer genome instability. Yet it is usually treated as a binary label 0⃣/1⃣ MSI-high or not.
Our new preprint – fresh from the oven🔥 -- argues that this simple dichotomy obscures the underlying biology.
Trouble with different parts of the MMR machinery is "recorded" in the genome sequence as distinct mutational patterns🧬. We worked out which pattern means what.
Amazing collaborative work led by Marcel McCullough and Adam Poti, with @maiamontagnarde Maia Munteanu, at @IRBBarcelona + @UCPH_Research
Bravo, team!
🤖 Made with AI
❓❔ Why does that matter?
We think “MMR-deficient” tumors are not all equivalent. Microsatellite instability, a widely used marker in cancer research and diagnostics, is better viewed as a multidimensional phenotype, not a simple MSI-H vs MSS switch.
The affected MMR gene changes the balance of substitutions, indels and DNA-repeat instability. These patterns reveal which repair activities remain and which backup routes have taken over.
For instance, the supposedly “lazy” MLH3 (whose role in somatic MMR has been unclear) does help safeguard the genome, serving as an understudy to the prima donna PMS2.
🤖 Made with AI
The mutational signatures successfully “travelled” to actual cancer genomes. Our predictive models could often identify which MMR gene broke down in a tumor.
We also think that subtle MMR defects can fly under the conventional MSI “radar”: ~10% of nominally-MSS tumors exhibited a MMR-intermediate (MMRi) state in WGS.
And, as a bonus, controlled MSH3 loss generated direct genome-scale evidence for the curious, long-debated EMAST tetranucleotide-repeat instability pattern, with related signals detectable in cancer genomes.
p.s. very happy the @qedScience AI-powered “validity engine” placed our study in the top 1% 🤩 We take reviewbot results with a grain of salt, but we like the outcome nonetheless😜
Proud of the team 🔝Marcel 🔝Adam 🔝 Maia!
The AI overlords have spoken! Our #NMDetectiveAI preprint (NMD genomics/deep mutational scans/mRNA language model; collab @BenLehner) seems to be in the top 1% by @qedScience 🤩
the-one-percent.qedscience.c…
many congrats to 1st co-authors @IgnasiToledano & @MarcellVeiner 👏👏
🎉 Meet The 1%.
QED Science evaluated 57,455 @biorxivpreprint life science preprints - blindly, at scale, and on the strength of the science itself.
Today, we’re celebrating the 574 manuscripts that rose to the top.
🏅 The 1%: the-one-percent.qedscience.c…
Good science deserves to be seen.
@openrxiv @cshperspectives
Fran Supek retweeted
📢📢 Job opening between the Bioinformatics Unit and my group (REPBIO) at @CRGenomica
Interested in contributing to our efforts to leverage long-read sequencing technologies for human genome research?
Join us in Barcelona:
recruitment.crg.eu/content/j…
Please repost😊
Fran Supek retweeted
📢 Hiring several postdocs in AI for RNA therapeutics!
Projects span:
- Explainable RNA language models
- Optimising coding & non-coding parts of mRNA sequences
- RNA 3D structure prediction
- RNA–small molecule binding
- Agentic AI In RNA biology
Strong AI track record needed; biology background a plus, not a must.
Please RT 🔁
🤖 Made with AI
✂️🧬preprint!
"Quantitative prediction of nonsense-mediated #mRNA decay across human genes by #genomic language #model & large-scale mutational scanning"
Fantastic teamwork by @IgnasiToledano👨🔬and @MarcellVeiner👨💻we combine massive experiment and sequence AI to elucidate NMD👇
Cells can destroy mRNAs with premature🛑stop, frameshift or splice #mutations via NMD pathway. But whether a given variant is silenced✂️is often predicted using a few hand-crafted rules. We asked: can this be modeled quantitatively, across human genes, w/both #AI and experiment?
Great collaboration between our @GenomeDataLab at @IRBBarcelona / @UCPH_Research and the @BenLehner group at @CRGenomica / @sangerinstitute
👉Read our NMDetective-AI study
biorxiv.org/content/10.64898…
Fran Supek retweeted
🧬 Hiring Postdocs at @astar_gis !
Help us decode cancer genomes. We need Computer Scientists and Computational Biologists to develop novel algorithms for de novo assembly of cancer genomes or to help us reconstruct them.
Experience in sequence alignment/assembly algorithms or de novo assembly of complex genomes required
Passionate about algorithmic puzzles with clinical impact? DM me.
Please RT! 🔄 #CompBio #Genomics #Algorithms #Postdoc
On the heels of the release of #AlphaGenome, we have written up a review on 🧬genomic language models for DNA/RNA, out in EMBO Press @MolSystBiol:
🔖"The DNA dialect: a comprehensive guide to pretrained genomic language models" by @MarcellVeiner (nice work!👏) & yours truly👇
The standard Transformer architecture is hitting a scaling limit in genomics. 🧱
A new review details the pivot to Hybrid Architectures (mixing Attention with SSMs like Hyena/Mamba) to efficiently model the 1Mb+ contexts vital for regulation.
But architecture isn't everything: specialized, supervised models still often outperform these massive generalist gLMs on long-range tasks.
link to our DNA language models review article at Molecular Systems Biology
➡️ link.springer.com/article/10…














