The reason large AI companies with virtually unlimited capital, compute, and computer science talent have not transformed biology is very simple. They do not understand biology. In biology, the hardest problem is not accessing information. We already have enormous amounts of publicly available data, papers, databases, omics datasets, and experimental results. Much of it remains underutilized. Most of it can be converted into computable formats straightforwardly. The difficult part is knowing which questions matter. What do we actually understand? What don’t we understand? Which hypotheses are worth testing? Which experiments would meaningfully reduce uncertainty? Where are the conceptual bottlenecks preventing progress? These are scientific judgment problems, not database problems. What biology lacks is not data. It lacks enough biologists with strong quantitative and computational skills who can identify important questions and use available tools to answer them. Biology is different from coding. The challenge is not retrieving information. The challenge is deciding what information is worth generating in the first place. Once AI companies recruit enough biologists who know how to identify important questions and how to use quantitative tools to address them, the opportunities will be enormous. The bottleneck is not the databases. The bottleneck is biological insight.

If you’re any good at picking the targets & getting sufficiently strong responses to them then the process would have a detectable survival benefit at scale. And if that doesn’t happen, process needs improvement (which is where the field is at right now)

The first dog vaccine in the US, by Merial, was against canine melanoma and received conditional licensure in 2007 and full licensure in 2010. It uses the sequence for human tyrosinase, which results in targeting cancer cells that overexpress tyrosinase.

Models have the default consensus view of everything. Your edge is anywhere you can identify that the default consensus view is wrong.

If a grey wolf engineered to have white fur and another 15 edits copying (part of) dire wolf genes = a dire wolf, then is a pig engineered to have several human genes with a total of 69 genomic edits = a human? hms.harvard.edu/news/first-g…

Yup

I need everyone’s help please We need 1000 views on this new $tsoi video by midnight so that it ranks on YouTube Please be so kind to help m.youtube.com/watch?v=EJLNRZ…

Social media vacillates between hype and hostility. But books are a steady source of clarity and common sense. The 12 SMARTEST BOOKS I read in 2022:

I paper I published on how the $TSOI #cancer #immunotherapy works oncotarget.com/article/15563…

The other 20% are bots. Of course a sentient bot might lie about this.

New research shows that the FDA’s Breakthrough Therapy Designation program reduced clinical development time by 23% without impacting drug safety. Going faster does not require being unsafe!

Posted before but this is really a good step as we move toward personalization Neoantigen TCR T-cell Therapy Shows Early Proof-of-Concept in Solid Tumors onclive.com/view/neoantigen-…

The oncogene bcr-abl can protect leukemic cells from #chemotherapy but cannot protect them from killing by natural killer cells...this is why we love these guys...and this is why $fate is so heavily invested. sciencedirect.com/science/ar…

Twitter is a great media for discussing #immunology #cancer #immunotherapy #stemcells and #regenerativemedicine

mTOR inhibition kills #cancer derived immune suppressive exosomes which have PD-L1 on their surface mdpi.com/2072-6694/14/17/408…

Chicken and the egg in type 1 diabetes??? Seems like islets start to die before onset of autoimmunity jci.org/articles/view/162123