RNA intelligence for precision medicine. YCS25
San Francisco
Joined June 2025
- Tweets19
- Following3
- Followers454
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Blank Bio retweeted
Really happy to welcome @WilliamOhMD as a scientific advisor to @blankbio_!
William’s career spans leadership roles at @DanaFarber, @MountSinaiNYC, @Sema4, and @PCFnews. He now leads Precision Medicine at @YaleCancer and @SmilowCancer.
He brings decades of experience caring for patients, leading cancer research, and translating molecular data into evidence, including work on blood-based RNA signatures in advanced prostate cancer.
At Blank Bio, we’re building models of patient biology using RNA to predict treatment response and clinical outcomes. By learning more from each patient, we want to make clinical trial abundance possible: more trials, each requiring fewer patients and focused on those most likely to benefit.
William brings the experience to help us connect those ambitions to the realities of drug development and patient care. Welcome, William! Really excited to build together.
blank.bio/william-oh-scienti…
RT @hsu_jonny: In Boston for WorldCDx this week! Excited to share early looks into our transcriptomic foundation model with a few partners…
Blank Bio retweeted
I joined @blankbio_ this week in SF! Now feels like a great time to be building foundation models for biology.
I'm excited by what these models would enable e.g. an understanding of gene expression data could better predict individual differences in drug response.
Blank Bio retweeted
We had a great time sponsoring @ASABiopharm RISW 2026 last week and speaking with biostatistics and clinical development leaders!
Many of our conversations focused on a question we think about a lot: how can we make clinical trials more efficient? One part of the answer is making better use of the clinical and biological data collected during trials. As more studies generate molecular data, foundation models can help extract meaningful biological signal and make more accurate predictions about patient outcomes, like survival.
These predictions can inform trial design and analysis to reduce uncertainty, build statistical power faster, and help trials reach their endpoints sooner.
If you’re interested in how biological foundation models could help shorten oncology trials, we’d love to hear from you. Let’s chat!
Blank Bio retweeted
We've made 20 years of RNA-seq searchable by concept. This improves agentic hypothesis generation: agents can reason in text and experimental spaces at once
Read on for agentic science and a sneak peek at Ladon, our transcriptomic foundation model!
How much better could AI agents get at scientific discovery if they could search in biological embedding space instead of text?
Over the past few months, we’ve been building Ladon, our bulk transcriptomics foundation model. As an experiment, we embedded over 1 million public RNA-seq samples spanning 20+ years of research, exposed that embedding space through an MCP, and let Claude search it.
Today, AI agents search titles, abstracts, metadata, and keywords. They can’t search the biology itself.
Ladon maps every RNA-seq sample into a shared biological embedding space, allowing agents to search by biological similarity instead of text. A naive search mostly retrieves studies from similar tissues or diseases. What we actually want to match is the transcriptomic signature itself: the pattern of what changed in a study, not the tissue or disease it happened to use. Searching on that lets an agent connect experiments across diseases, tissues, and perturbations, even when the papers share no keywords.
We explored a case study where this surfaced novel hypotheses that would have been difficult to find through traditional search with agents.
Read more in the blog: blank.bio/ai-agents-will-acc…
If you're sitting on large RNA-seq datasets and want to harmonize them with the public transcriptome to power agent-driven discovery, we'd love to collaborate. Reach out!
In the latest blog from the team, we share an early look at Ladon, our bulk transcriptomics foundation model. We embedded more than one million public RNA-seq samples spanning over 20 years of literature, exposed that embedding space through an MCP, and let Claude search it.
Our goal was to explore how much better AI agents could get at scientific discovery if they could search in biological embedding space instead of text.
Read the blog here: blank.bio/ai-agents-will-acc…
How much better could AI agents get at scientific discovery if they could search in biological embedding space instead of text?
Over the past few months, we’ve been building Ladon, our bulk transcriptomics foundation model. As an experiment, we embedded over 1 million public RNA-seq samples spanning 20+ years of research, exposed that embedding space through an MCP, and let Claude search it.
Today, AI agents search titles, abstracts, metadata, and keywords. They can’t search the biology itself.
Ladon maps every RNA-seq sample into a shared biological embedding space, allowing agents to search by biological similarity instead of text. A naive search mostly retrieves studies from similar tissues or diseases. What we actually want to match is the transcriptomic signature itself: the pattern of what changed in a study, not the tissue or disease it happened to use. Searching on that lets an agent connect experiments across diseases, tissues, and perturbations, even when the papers share no keywords.
We explored a case study where this surfaced novel hypotheses that would have been difficult to find through traditional search with agents.
Read more in the blog: blank.bio/ai-agents-will-acc…
If you're sitting on large RNA-seq datasets and want to harmonize them with the public transcriptome to power agent-driven discovery, we'd love to collaborate. Reach out!
Blank Bio retweeted
High-resolution AI models require high-resolution data. We are excited to partner with @blankbio_ as they use #PacBio HiFi long-read RNA sequencing to unlock greater interpretive power for precision oncology.
Read the full announcement here: bit.ly/3Ps0GkB
Today, we’re excited to announce our $7.2M seed round alongside a strategic collaboration with @PacBio! At Blank Bio, we help pharma make more informed decisions in clinical trials by capturing the biology that makes each patient’s tumour unique.
We do this by training RNA foundation models to learn the patterns that shape disease progression and how patients respond to treatment. In our collaboration with @PacBio, we will generate long-read, bulk RNA-sequencing data from patient tumour samples across multiple indications. This dataset will be used to further train and evaluate our models.
Our models are currently focused on two main applications. First, we predict how a patient’s disease is likely to progress over time from their molecular profile. Through the FDA-recommended approach of covariate adjustment, these RNA prognostic scores can help clinical development teams design trials that reach statistical power with fewer patients. Second, we’re working to help pharma teams understand biological differences between patients so they can identify who is most likely to respond to a given therapy.
We are thankful to @definevc, @LeonisCapital, Nova Threshold, @ripple_ventures, @SignalFire, @ycombinator, and other investors for being early supporters in our vision. This financing will support the continued development of our foundation models, expanded collaborations with pharma, and growing our patient-level datasets.
Read the press release: blank.bio/seed-announcement
Read the blog from the team: blank.bio/accelerate-clinica…
Learn more about Blank Bio: blank.bio
Blank Bio retweeted
Congrats to @blankbio_ on their $7.2M seed and strategic collab with PacBio!
They're training foundation models on bulk RNA-seq data to help pharma teams design better clinical trials.
Good drugs fail when their effect gets lost in patient variability, or when trials enroll patients whose biology won't respond. Blank Bio's models learn that heterogeneity to build prognostic and predictive biomarkers from tumor transcriptomes.
blank.bio/seed-announcement
Blank Bio retweeted
Thanks to Huanjia Zhang for covering our story today in @GenomeWeb! The article highlights our seed round, our strategic collaboration with @PacBio, and our work in building RNA foundation models to accelerate clinical trials
genomeweb.com/informatics/bl…
Today, we’re excited to announce our $7.2M seed round alongside a strategic collaboration with @PacBio! At Blank Bio, we help pharma make more informed decisions in clinical trials by capturing the biology that makes each patient’s tumour unique.
We do this by training RNA foundation models to learn the patterns that shape disease progression and how patients respond to treatment. In our collaboration with @PacBio, we will generate long-read, bulk RNA-sequencing data from patient tumour samples across multiple indications. This dataset will be used to further train and evaluate our models.
Our models are currently focused on two main applications. First, we predict how a patient’s disease is likely to progress over time from their molecular profile. Through the FDA-recommended approach of covariate adjustment, these RNA prognostic scores can help clinical development teams design trials that reach statistical power with fewer patients. Second, we’re working to help pharma teams understand biological differences between patients so they can identify who is most likely to respond to a given therapy.
We are thankful to @definevc, @LeonisCapital, Nova Threshold, @ripple_ventures, @SignalFire, @ycombinator, and other investors for being early supporters in our vision. This financing will support the continued development of our foundation models, expanded collaborations with pharma, and growing our patient-level datasets.
Read the press release: blank.bio/seed-announcement
Read the blog from the team: blank.bio/accelerate-clinica…
Learn more about Blank Bio: blank.bio
Orthrus is now published in Nature Methods. Congrats to @phil_fradkin and @ianshi on the work!
We'll be #AACR2026 over the next few days. Drop us a note if you want to chat about how we're extending these ideas to build the next generation of models for clinical trials.
Excited to share Orthrus is now published in Nature Methods! This was a work from our PhDs in which we showed 3 things:
- There's lots of room for new biologically grounded self-supervised objectives
- The "y - intercept" in scaling is important! We show that representations from 10 million parameter Orthrus outperform a 7 billion parameter model, 700 its size.
- Orthrus works in the low-data regime where data acquisition is especially expensive: low throughput experimental data and clinical trials
Ian and I are now building BlankBio to apply these ideas at a bigger scale. I'm going to be at AACR get in touch if you want to chat!
We'll be at #neurips2025! Come join us and the @SignalFire team at our ML x Bio Mixer on Dec 4th at 6 PM.
We're also hiring. Check out our open roles: ycombinator.com/companies/bl…
Excited to co-host an ML × Bio Mixer at #neurips2025 with @SignalFire!
Join us Dec 4 @ 6 PM for food, drinks, and good convo with others building at the ML × bio frontier. Plus, we’re hiring!
Open roles: ycombinator.com/companies/bl…
RSVP: luma.com/1ycvcndz
Blank Bio retweeted
Will scaling current nucleotide foundation models lead to a GPT-3 moment for biology? Probably not.
Although models like Evo2 are impressive in many ways, their pre-training objectives can struggle to outperform supervised learning.
I dive into why at: quietflamingo.substack.com/p…
Blank Bio retweeted
Programming mRNA can make it the most exciting biotechnology but we first have to learn how
We're building the tools to make it happen @blankbio_
Blank Bio retweeted
A new AI research lab called @blankbio_ launched today. They are building new foundation models for mRNA, and are one of the strongest technical teams I've worked with.
I'm particularly excited about their new model results, and the wide set of application areas for it.
Blank Bio (@blankbio_) is building foundation models to power a computational toolkit for RNA therapeutics, starting with mRNA design and expanding to target ID, biomarker discovery, and more.
ycombinator.com/launches/O8Z…
Congrats on the launch, @hsu_jonny, @phil_fradkin & @ianshi3!
Blank Bio retweeted
RNA is an untapped modality with tremendous amounts of potential. I'm excited to share more about the vision we're building towards @blankbio_!
Learn more about our foundation models and the tools we're building to accelerate RNA-based therapeutics: blank.bio/
Blank Bio (@blankbio_) is building foundation models to power a computational toolkit for RNA therapeutics, starting with mRNA design and expanding to target ID, biomarker discovery, and more.
ycombinator.com/launches/O8Z…
Congrats on the launch, @hsu_jonny, @phil_fradkin & @ianshi3!
Blank Bio retweeted
The news is out! We're starting Blank Bio to build a computational toolkit assisted with RNA foundation models. If you want to see my flip between being eerily still and overly animated check out the video below!
The core hypothesis is that RNA is the most customizable molecule in the world. It's fast to produce, inherently programmable, and highly adaptable to diverse use cases. It's an open secret amongst folks who've worked at Deep Genomics, and that's why almost everyone is still working on RNA applications.
We're building out the next generation of tooling enabled by our foundation models and partnering with companies to accelerate their work. If you have a use-case big or small we'd love to chat, please reach out.
The foundation models we're building stem from intuition acquired through years of research from our PhDs. We learned that effective biological models require deep understanding of data, specific architectures and loss functions that reflect the molecular processes shaping the data. Genomics metrics like conservation and constraint inform model development.
One of the best parts about this is that I get to work with such talanted, kind, ambitious people. @hsu_jonny has spent the last five years defining what great ML+Bio products look like and consistently pushes us to adapt to customer needs with mission oriented clarity. @ianshi3 is the most thoughtful and creative scientist I've ever worked with, bringing a great sense of design to everything he does.
We're on a mission to build something great and useful for RNA scientists around the world.
blank dot bio
Blank Bio (@blankbio_) is building foundation models to power a computational toolkit for RNA therapeutics, starting with mRNA design and expanding to target ID, biomarker discovery, and more.
ycombinator.com/launches/O8Z…
Congrats on the launch, @hsu_jonny, @phil_fradkin & @ianshi3!
Blank Bio retweeted
Blank Bio (@blankbio_) is building foundation models to power a computational toolkit for RNA therapeutics, starting with mRNA design and expanding to target ID, biomarker discovery, and more.
ycombinator.com/launches/O8Z…
Congrats on the launch, @hsu_jonny, @phil_fradkin & @ianshi3!
Blank Bio retweeted
Really excited to share that we’re in the @ycombinator Combinator summer batch!
I’m even more excited to be teaming up with @phil_fradkin and @ianshi3 on this next chapter.
@blankbio_, we're building the next generation of foundation models for RNA.
