Jeremie Kalfon 👨‍💻🧬🤖🚀
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jkobject.com
Jeremie Kalfon 👨‍💻🧬🤖🚀
@jkobject.com
690 followers 3.3K following 130 posts
Doing a Ph.D. AI in Bio. | Ex @WhiteLabGx @BroadInstitute @MIT | Built @PiPleteam | ML, Cancer, Genomics, Data Sci, Entrepreneur, FullStack Dev | All views are mine
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🚨🚨 AI in Bio release 🧬
Very happy to share my work on a Large Cell Model for Gene Network Inference. It is for now just a preprint and more is to come. We ask: “What can 50M cells tell us about gene networks?”❓
Without double-talk and with amazing panelists🧑‍🔬:

- Yann Fleureau, CEO, Blossom Life Sci & Founder of Cardiologs
- Steven Jerome, Director, Lead of Hit Discovery, Schrödinger
- Jérémy Besnard, Advisor, InFocusTx & Co-founder of Exsciencia
- Sofia Dahoune, Partner at Daphni
2/3
🌐🧬I am excited to present you a round table I am doing together with Matteo Marengo Gabriel Michaux as part of our emerging Nucleate Parisian chapter led by Clara Brouaux 🔥.

Title: **Inside AI: Choosing the Right Path to Value Creation**
1/3
I am presenting my PhD work today at the conference on immuno oncology in Toulouse's CRCT Oncopole!

Happy to talk about how we can use foundation models in the real world 🧬 🧑‍⚕️
🔷 Alnylam BioVenture Challenge — one day at Alnylam HQ, one shot at $100K in non-dilutive funding. Apply by Oct 17.

And — we’re also recruiting the next generation of Nucleate Leaders. If you’re ready to build biotech and strengthen the community behind it, apply today.
It’s about growth, collaboration, and the chance to give back by lifting others.
Two flagship opportunities are now open:

🔷 Activator 2026
— our equity-free accelerator equipping scientific founders with the tools to launch biotech ventures. Apply by Oct 20.
Proud to be a Nucleate Leader! 🚀

Being a Nucleate Leader means joining a community of peers who step up to shape the future of biotech — leading teams, driving programs, and building ventures that make real impact.
The first 1 million prime numbers vizualized in 2D according to their prime factors (Umap)

Source: johnhw.github.io/uma...
what they sell you, what you get...
If you are launching your biotech / techbio startup in Paris or anywhere else in the world actually, think about applying to Nucleate's Global Activator Program! 🚀 🧬

Many people to meet and things to learn from Researchers, Investors, CEOs and more!
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Made with Softr, the easiest way to turn your data into portals and internal tools.
gateway.nucleate.org
Of course, but you first need the info. Right now it is like driving a car blindfolded...
We then spend hundreds of billions treating what could have been avoided.

Why aren’t we doing this by default?
2/2
In 2025, deciding to have a child without full genome sequencing of both parents is borderline reckless.

It costs under €400. Takes 2 minutes. Could save your child’s life.

Yet >200 million people live with rare genetic diseases—many preventable by this simple test.
1/2
I am at ISMB ECCB this week 🧬👨‍💻Do reach out if you are in Liverpool and want to chat about AI, target discovery and disease modelling 😊
If you are at ICML, I would be happy to meet to talk about AI, bio, and drug discovery!
Lots of work remain: (1) We only show this ability on protein→cells. (2) We haven’t used other fine tuning methods than adapter layers for now.

I would love to talk about these ideas with people and will be at ICML in Vancouver and ISMB/ECCB in Liverpool! ✈️
We show that this helps the models learn faster, achieve better results on many test metrics and create better representations.

This is an early proof of concept toward this grand goal of modeling life across scales.
By using common fine-tuning mechanism we show how one can train from one scale to the next by back-propagating signal to the compressed tokens and lower scale model.
By using cross attention and an auto-encoding mechanism we present XPressor, a framework that creates compressed tokens from a scale (e.g. proteins) that can be used as inputs tokens by the scale above (e.g. cells)
Each group of biologist are on their own niche and so too are the models. But These models talk about different steps of the same stair.

We present ideas on how we might end up training models from atoms to organs by using transformers to compress 🔺 🔻 data into tokens used by larger scale models
Very happy to share that my new paper got accepted at the ICML workshop for Foundation model for Life Sciences!!
www.biorxiv.org/cont...

Foundation Models are being trained from atoms to molecules ⚛️, molecule chains 🧬, entire cells 🦠, and even groups of cell across tissue slices 🫁