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The business of extracting knowledge from academic publications

theseedsofscience.pub

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Re: The business of extracting knowledge from academic publications

#41

Post by riku_iki »

myownpetard wrote: post #40
riku_iki wrote: post #32

Earlier quoted context omitted.

You can just ignore my comments and walk away? To me you just another "internet expert" which I am not sure why should I blindly trust.

Your sources are talking about the ratio of small molecule vs. large molecule drugs. Even if you're developing small molecule drugs you are likely targeting some aspect of protein signaling/gene expression. People are being dismissive of your comments because to say that proteins are niche in the context of pharma is like saying advertising is niche in the context of Meta and Google.

> People are being dismissive of your comments because to say that proteins are niche in the context of pharma is like saying advertising is niche in the context of Meta and Google.

its all about how you define word "niche", for google, main revenue stream is supported by several pillars: search tech, infra tech, ads tech, ecosystem+network effect, human management. You remove one pillar, and everything is destroyed, so one can say ads is one of the niches in their food chain. I suspect with proteins it is about the same.

> in the context of pharma

there is no context of pharma. Post is about more broad bio-medical publications.

Re: The business of extracting knowledge from academic publications

#43

Post by wodenokoto »

pfisherman wrote: post #14

Word of advice to all those who are chomping at the bit to disrupt pharma with AI. A pharmaceutical company that heavily leverages computation is called a pharmaceutical company. All modern pharmaceutical companies heavily leverage computational tools - including some powered by deep learning. Any company building a computational platform to accelerate drug discovery / development is not a pharmaceutical company. The…

I did a stint at large pharma, and it was not free software that was used to extract structured data from publication.

Re: The business of extracting knowledge from academic publications

#44

Post by myownpetard »

riku_iki wrote: post #41
myownpetard wrote: post #40

Earlier quoted context omitted.

Your sources are talking about the ratio of small molecule vs. large molecule drugs. Even if you're developing small molecule drugs you are likely targeting some aspect of protein signaling/gene expression. People are being dismissive of your comments because to say that proteins are niche in the context of pharma is like saying advertising is niche in the context of Meta and Google.

> People are being dismissive of your comments because to say that proteins are niche in the context of pharma is like saying advertising is niche in the context of Meta and Google. its all about how you define word "niche", for google, main revenue stream is supported by several pillars: search tech, infra tech, ads tech, ecosystem+network effect, human management. You remove one pillar, and everything is destroyed,…

> Word of advice to all those who are chomping at the bit to disrupt pharma with AI.

Literally the first line in the comment that started this thread.

Re: The business of extracting knowledge from academic publications

#45

Post by riku_iki »

myownpetard wrote: post #44
riku_iki wrote: post #41

Earlier quoted context omitted.

> People are being dismissive of your comments because to say that proteins are niche in the context of pharma is like saying advertising is niche in the context of Meta and Google. its all about how you define word "niche", for google, main revenue stream is supported by several pillars: search tech, infra tech, ads tech, ecosystem+network effect, human management. You remove one pillar, and everything is destroyed,…

> Word of advice to all those who are chomping at the bit to disrupt pharma with AI. Literally the first line in the comment that started this thread.

Sure, now let's read the post?

Re: The business of extracting knowledge from academic publications

#47

Post by weswilson »

On the medical side, there are knowledgebases that offer clinical decision support like UpToDate (https://www.wolterskluwer.com/en/solutions/uptodate) that are kept up to date (pun intended) by specialists in their field. Every year or so, the articles are reviewed and updated with new information that has integrated into practice. For a relatively small fee, a practitioner has pretty much all access to the latest evidence-based standard of care across any specialty. UpToDate is also a commercial product. With a claimed 2+ million subscribers at roughly $200-500/yr, there is clearly money out there for a well made product.

In regards to the article, parsing academic publications and spitting out a word cloud or k-nn graphs of topics isn't going to be useful to a professional. They've already built up a working model in their mind that they've honed over the years. They have years of filtering information and the ones contributing to these knowledgebases have the experience to curate that information to professionals which is what's lacking from these NLP experiments.

I do think that ML and tools like SemanticScholar can be used to identify new literature that may affect knowledgebase articles and flag them for review. I'd be surprised if that doesn't already exists to some extent.

Re: The business of extracting knowledge from academic publications

#48

Post by bigger_cheese »

YossarianFrPrez wrote: post #9

This was a fascinating read. The well-meaning and hard-working author went through several iterations of trying to make a profit on 'academic-knowledge-graph-adjacent' products, but things ultimately fell through. The article describes two separate things likely to appeal to HN readers. The first is that there is a lot of tacit knowledge not captured in scientific publications. The second is that the author and his t…

I'm an Engineer (not a scientist) not sure how much is applicable to other fields but when I've been interested in what the "current state of topic X is" I have looked for recently published thesis on the topic, a good thesis will have summarized the papers for me (gone through all the churn of papers and highlighted the key points).

I think this is kind of what the author attempted to build (i.e. something that spits out the Literature review portion of a thesis.)

I think that's probably why graduate students were excited - they are the people who have to write a thesis at the end of the day.

Re: The business of extracting knowledge from academic publications

#49

Post by hbcondo714 »

riku_iki wrote: post #30
Etheryte wrote: post #22

Earlier quoted context omitted.

Competition is good, it means there is a market. There's plenty of empty niches with no competition exactly because there's no money there.

As for corporate fillings, there are lots of strong products already, also data in those fillings is rather limited. "Business intelligence" is very broad term, maybe it is possible to find market fit there, since area is moving very fast, but hard to judge without seeing specifics.

I happen to run a corporate filings product[1] so I'm curious to know in what way you find the data in filings limited. There are financial statements (ex. balance sheet) & disclosures (ex. litigation) in 100+ page annual reports so our tool makes it easier to find them. We also do AI (ex. sentiment analysis) and diffs (ex. redline / blackline) which yield their own insights.

[1] https://last10k.com

Re: The business of extracting knowledge from academic publications

#50

Post by riku_iki »

hbcondo714 wrote: post #49
riku_iki wrote: post #30

Earlier quoted context omitted.

As for corporate fillings, there are lots of strong products already, also data in those fillings is rather limited. "Business intelligence" is very broad term, maybe it is possible to find market fit there, since area is moving very fast, but hard to judge without seeing specifics.

I happen to run a corporate filings product[1] so I'm curious to know in what way you find the data in filings limited. There are financial statements (ex. balance sheet) & disclosures (ex. litigation) in 100+ page annual reports so our tool makes it easier to find them. We also do AI (ex. sentiment analysis) and diffs (ex. redline / blackline) which yield their own insights. [1] https://last10k.com

> so I'm curious to know in what way you find the data in filings limited.

to me every filling has maybe 20 essential numbers which are interesting: balance sheet, income statement and major sectors, everything else is some generic boilerplate, and there are dozens of services which will already sell it for cheap.

Not sure what else you can sell to your clients..

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