Andrew Leduc
1.9K posts
@_AndrewLeduc
Post-doc @slavovlab Interests: Non-canonical protein sequences Protein degradation Single cell analysis
2. TIME: Tracking temporal effects is absolutely crucial. Tracking beginning & arbitrary end states only makes it impossible to trace the dynamics of cell state transitions, critical intermediate states, direct vs indirect effects, feedback & compensation etc. 5/
Great men of history had little to no introspection. The personality that builds empires is not the same personality that sits around quietly questioning itself. @pmarca and I discuss what we both noticed but no one talks about: David: You don't have any levels of introspection? Marc: Yes, zero. As little as possible. David: Why? Marc: Move forward. Go! I found people who dwell in the past get stuck in the past. It's a real problem and it's a problem at work and it's a problem at home. David: So I've read 400 biographies of history’s greatest entrepreneurs and someone asked me what the most surprising thing I’ve learned from this was [and I answered] they have little or zero introspection. Sam Walton didn't wake up thinking about his internal self. He just woke up and was like: I like building Walmart. I'm going to keep building Walmart. I'm going to make more Walmarts. And he just kept doing it over and over again. Marc: If you go back 400 years ago it never would've occurred to anybody to be introspective. All of the modern conceptions around introspection and therapy, and all the things that kind of result from that are, a kind of a manufacture of the 1910s, 1920s. Great men of history didn't sit around doing this stuff. The individual runs and does all these things and builds things and builds empires and builds companies and builds technology. And then this kind of this kind of guilt based whammy kind of showed up from Europe. A lot of it from Vienna in 1910, 1920s, Freud and all that entire movement. And kind of turned all that inward and basically said, okay, now we need to basically second guess the individual. We need to criticize the individual. The individual needs to self criticize. The individual needs to feel guilt, needs to look backwards, needs to dwell in the past. It never resonated with me.
🚨 This is how taxpayer-funded academia is quietly handed over to ideological factions hostile to the United States: Richard Lyons is the Chancellor of UC Berkeley. His salary is $946,445 with perks that total an extra $2 million. His campus is targeting 16 professorships for H-1B hires in fields like music, business, and economics, positions that qualified Americans could fill. The payout for these positions total millions in federal funds. UC Berkeley fleeced Americans for $419 MILLION in federal funding last year. They're using your money to pay foreign professors.
OP says UC Berkeley 'fleeced' the taxpayer out of $419 million. Here's what some of that fleecing has gotten our country lately ... 1) CRISPR (gene-editing therapy invented at Berkeley) used to provide personalized medical care to a baby with a rare and fatal genetic disease. 1/
This is false. RT-PCR of a gene-of-interest is an independent replicate. Even if the replicate is less accurate, it's still of value, since in many studies (e.g. scRNA-seq) there are no replicates at all (!) Of course ideally one designs good experiments from the outset.
The correlations get scrambled, even the highest-magnitude ones, and many of them flip! This finding is profoundly troubling. Standard procedures, used in thousands of papers, tremendously distort one of the strongest signals in the data. 9/
Monod fits biophysically motivated models to single-cell transcriptomics data, providing insights into gene expression dynamics. @goringennady @lpachter @mariacarilli @johnjvastola nature.com/articles/s4159…
I am looking for a new industry role in computational biology! Check out my portfolio of genomics, statistics, ML, and biophysics work at gennadygorin.github.io, and reach out if you have any suggestions or open roles!
Just encountered a fun example of primitive LLM reasoning. I have been doing some proteogenomic analysis of CPTAC data. quantifying missense mutations. The proteomics samples are multiplexed so that ~10 different samples are run together... 🧵
The lack of calibrated confidence of AI output & a lack of understanding of how this confidence changes in different domains with every update is one of the biggest risks of using AI for science at scale especially by those without deep expertise. 1/
