rbartelme

As a bioinformatics person that's spent time in and out of industry/academia, I agree with some of the article's thesis. While I don't think LLMs or AI are going away, I do think it will allow people in academia to pump out a bunch of inane papers and continue to prop up predatory scientific journal publishing via tenure and promotion. In fact outside of how utterly useless Fable 5 is via their aggressive guard rails for my work, I quite like using statically typed and/or functional languages with other LLMs since there are some baked in guardrails via compiler + type system.

I think the flattening of progress is the most interesting dimension to the article. For an example a useful biological product discovery with a nonlinear path to get to there, look at the Taq polymerase (https://en.wikipedia.org/wiki/Taq_polymerase). Without some NSF funded exploratory ecological research by Tom Brock in Yellowstone Hot Springs to test the theoretical limit of life at high temperatures (https://en.wikipedia.org/wiki/Thermus_aquaticus) we never get to the Taq polymerase, we never get reliable/robust PCR (https://en.wikipedia.org/wiki/Polymerase_chain_reaction), which is now a gold standard method in both clinical and environmental testing! It is rather improbable to think that large language models would associate those domain connections across the topic (molecular biotechnology + ecology + microbial physiology). I also did some exploratory work with text embedding models people might use for RAG and challenged them with an open source scientific MCA question dataset, generalist embedders performed worse vs. domain specific embedders trained on scientific corpora (doesn't surprise me at all). However, if everything regresses to the median of the universe of possible knowledge, it seems like scientific leaning frontier models would get locked into this asymptotic flattening before turning cashflow positive for model vendors OR they become so locked down that only big pharma, state actors, or big ag can afford the API rates and vetting process.

Yeah, I definitely do something similar with my personal projects.

I come from more of a hardware & environmental engineering background and we were always taught that projects were iteratively built via Design, Build, Test, Learn cycles.

I drive the Design and basic skeleton of the build (pseudocode or boilerplate), then pass off the rest of the Build and Test to the agent. I pick up after the test and read the agent commits/notes, then write up next steps. Repeat DBTL. Maybe spin a few features out at a time in parallel depending on how much time I want to devote to reviewing new project features later in the day.

I wonder if the same thing happened with--or is happening at--NSF? I know researchers that did not get funding for quantitative ecology fellowships or grants. After back channeling with program managers, it seems that using "diversity"--as in the quantitative ecological measures, metrics, or derived functional values--may have flagged proposals to be rejected.

https://en.wikipedia.org/wiki/Alpha_diversity https://en.wikipedia.org/wiki/Beta_diversity https://en.wikipedia.org/wiki/Gamma_diversity https://en.wikipedia.org/wiki/Zeta_diversity

Pretty cool bioinformatics algorithm to speed up what was traditionally a dynamic Burrows-Wheeler Transform. Interested to see where this gets implemented outside of benchmarking in the next few years!

Thanks for the perspective. This makes me think I was a bit quick to judge their methodologies.

It's somewhat interesting, but the authors' conclusions are a bit odd given their data.

They acknowledge that fame is potentially confounding: Risk factors (impulsivity, substance use, etc.) -> Fame achievement | Risk factors -> Early mortality

The authors also appear to conclude that fame is semi-causal of the mortality risk. If, taking a causal statistical approach, the authors conditioned on the collider:

Risk factors (substance use, personality traits, mental health vulnerabilities) -> Becoming/staying a professional singer <- Talent/drive toward fame

I do applaud them for preregistering the study, but I think this paper needed a little more rigor in peer review.

Might be coming soon based on this: https://docs.rustfs.com/features/replication/

Outside bioconductor or the tidyverse in R can be just as unstable due to CRAN's package requirements.

This is a non-issue with Polars dataframes to_pandas() method. You get all the performance of Polars for cleaning large datasets, and to_pandas() gives you backwards compatibility with other libraries. However, plotnine is completely compatible with Polars dataframe objects.

Life's two most fundamental properties are homeostasis and reproduction. The loss of these two combined with its parasitic nature makes this cell a form on non-life.

This is a decidedly Eukaryote-centric take. Homeostasis in higher mammals is a complex network of genes -> RNA -> proteins -> metabolic pathways

Reproduction is also far more simple in organisms with binary fission cellular division.

A more appropriate scientific term would be obligate commensalism vs. "parasitic". That actually encapsulates their need for metabolic precursors from the host, but allows for tRNA, rRNA, origin of replication, etc...present in the organism's genome.

For all the folks saying, "Isn't this just a virus?"

The actual paper states that the genome encodes transfer RNA's and ribosomal RNA's. I think that's a really important biological distinction missing from the popular press junket. The primary source material is well written and elucidates a lot more than the Quanta article. https://www.biorxiv.org/content/10.1101/2025.05.02.651781v1

If you're interested in this topic, I'd highly recommend checking out Michigan State's E coli Long-term Evolution Experiment: https://lenski.mmg.msu.edu/ecoli/index.html

Yeah I think this is definitely the future. Recently, I too have spent considerable time on probabilistic hyper-graph models in certain domains of science. Maybe it _is_ the next big thing.

Sometimes I miss the benchwork, but moving into bioinformatics/data science full-time has been much better for my physical and mental health.

Countable is a relative term in microbiology. I like that the author stuck to the phrase "countable colonies", since colony forming units are not really "countable as cells".

Allan Konopka does a good deep dive into "The Great Plate Count Anomaly" here: https://thinkmicrobe.substack.com/p/the-great-plate-count-an...

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This seems really interesting for local LLM experimentation.

The replication crisis is a growing concern.

This! The amount of clinicians I know who simply read the abstract of a case study, with no real statistical interpretation of results, is a non-zero number.

Whenever I see some hyped up popular press article about a scientific study, my immediate reaction is to go to the primary literature. First, I read the study design and analysis methods, then I determine if its even worth continuing to read the rest. Study pre-registration should be a must and papers need to be more explicit about being exploratory when the sample size dictates it.

I agree with all of this. I've worked in optical engineering, bioinformatics, and data science writ large for over a decade, knowing the data collection process is foundational to statistical process control and statistical design of experiments. I've watched former employers light cash on fire chasing results from similar methods this MCP runs on the backend due to lack of measurement/experimental context.

This MCP agent still doesn't defend the statistically illiterate from themselves.

This professor was, quite literally, on a teaching only contract.

I recently made the transition from academia to industry (also a PhD holder). I would echo a lot of what other commenters said about learning python and the associated data science tooling. Coming from an R heavy scientific discipline (quantitative ecology), I found python to be quite a bit better at things that base R struggled with, particularly string manipulations.

Aside from programmatic and cloud tools as identified in your post, one of the biggest hurdles is whittling down your academic CV into a resume. Spending time re-framing your academic accomplishments in the short form will be the best time investment for getting in for interviews. I ended up following the google XYZ resume formula: https://www.inc.com/bill-murphy-jr/google-recruiters-say-the... It kind of hurts to distill your academic achievements into "Published [X] peer reviewed papers [Y] by driving the analysis [Z]", but I think it really helped me start getting calls vs. desk rejects. Relatedly, only include publications that either highlight your expertise for a specific job posting or if they further highlight your expertise in statistics in a way that could set you apart from other candidates.

"Grant Applications:

Very specific academic "skill" which is intimately tied to the bureaucracy of the university system of your particular country or region. It also has a lot to do with things that aren't even in the application: who you know and what ? they think of you. Whatever org you join later will have some other way for funding to come down, and there are so many varieties of org that you'll just have to learn on that job."

I think the country/region part you mention is most relevant here, but I disagree with the overall assessment of grant writing being irrelevant. If you work in a start-up or the R&D division of a company, there's still grant money to be had. This lets companies pursue high risk projects while not jeopardizing overall revenue.

Any PhD worth their salt would know that a much more likely reason why their hard skills were valuable had nothing to do with PhDs and everything to do with them as a person being able to complete a PhD.

I often equate my Ph.D. with the ability to teach myself how to do things. Ex. going from a wet/dry lab biologist with zero experience in C-style languages, to learning Arduino's flavor of Cpp and the PID control library to run process controls for your wet lab biology experiment.

Cost/benefit analysis may dictate that, as other posters suggested, you'd be better served to get raw fastq files from a sequencing lab. Even better if you can send the lab a sample and they'll process the extractions for extra $$.

I think it might depend on whether or not U.S. insurance companies are involved. If so HIPAA is a real liability as a company. For example, a former colleague worked at a high profile private research facility that had their HIPAA firewall breached and their entire department was fired. Assuming that chat logs on these apps could fall into that same class of data in the U.S., that's a huge amount of risk/liability as a company.

https://archive.md/pog41

This is an extreme example of what can happen in a "company town". https://en.wikipedia.org/wiki/Bay_View_massacre

Running a Hidden Markov Model against shotgun metagenomic datasets != culturing microorganisms that can "do the thing".

Link to actual journal article: https://journals.asm.org/doi/10.1128/mBio.02155-21

"Less severe" as in less hospitalizations and death. The long-term effects of comorbidities are still being hashed out. A fair amount of the literature surrounding pulmonary megakaryocytes, platelets, and covid, is pretty alarming.

I guess my whole point was if they're trying to explain a mechanism, their sample size doesn't generalize to the population; even limiting it to the obese population. If anything they've shown, in their small cohort, that the hypothesis is worth further study.

Having spent a lot of time in academia before moving into biotech, I think this is a pilot study and they're fishing for more research funding. The way those funding mechanisms work, I tend to view manuscripts like this with a critical eye.