䷉Table: Companies in Knowledge Discovery (mostly Biology)
A sporadically updated table of companies I come across. The main focus is on life sciences, knowledge discovery, domain specific search, automated reporting.
In random order.
Table published from Roam through ivywrite
|
kd/comment |
JTBD |
---|---|---|
UIs not great, but good better granularity and molecular biology focused features and [[Named Entity Recognition | NER]] I’m short on it. | Detect biomedical concepts in text data with PhD accuracy at Big Data scale support for ever-evolving vernacular, ontologies and identifiers inform hypothesis generation and validation | |
The platform applies knowledge graph, AI and unique valuation technology to reveal entities of interest beyond the tacit knowledge of a scientist or team of scientists to spark novel insights and drive innovation. Cosmetics, Agritech, Food, Personal Care, Health Care | automated data scientist for biologists | |
Paper and Patent Search with relatively good Entity Resolution and Features | ||
I do not understand Golden. Seems like a glorified wiki with extra UI sponsored by a16z VC $$$. Nobody I know uses it. Possibly good for [[company search]] Has some proprietory data for startups and funding rounds, so it's probably most valuable to VCs and Buzzfeed employees | privatized [[Wikipedia]] with good UI? | |
Elsevier's Services | I guess it has the problems of any [[designed by committee]] product offering | still have to research [[Elsevier]] offerings in detail |
Reaxy | ... enterprisey | |
Their development speed is questionable (started already in 2015 -- didn’t have product for first 3 years) Most of their website is about their advisors and investors; pretty much nothing on the product | NLP discovery and drug target prediction for parkinson's | |
**Apparently used by 19/20 Pharma Corps which makes it by far the most established product. ** Closed-off trials; sales teams; not for consumer | NLP for drug repurposing, [[Patent Search]], population health, quality measures etc. | |
Seems to have prominent users Sent a Galactic (their product) self-signup user demo to [[@Sean Glaze]] a while back - he said it was not very helpful...hard to tell | comprehensive and up-to-date curation of biomedical research | |
Extremely stacked team, who authored many interesting papers I've read Impoverished interfaces. Seems to not a self-serve solution, but mostly a consulting SaaS | Provide [[agriculture]] with insights into the changing research landscape and its impact on R&D priorities through NLP focused on soil health, agriculture and food management | |
virtual cell simulation? | ||
Drug discovery AI for rare diseases | ||
longevity + ML | ||
instabase | billion dollar company | document structuring + workflows; similar to hyperscience.com |
... | open source biomedical database | |
Wolfram is ramping on life science knowledge base One of the most impressive systems I know. Calculations on low level physical, mathematical phenomena. Great for open question-answering (deployed by siri and google) | scientific computing, automated reporting, open-ended question-answering | |
Their whole "AI" should take six months, not three years. It's marginally useful and [[> provenance tracking]] of [[citance]]s (citing a fragment of a paper) is interesting to us. Good CEO, mediocre tech team. | ||
Yiannis and Artur are a very strong CEO+CTO team Seems they had some trouble to hire great senior talent for some reason (which is common). The only life science knowledge discovery team I know that actually shows their UI (which is very well done) | ||
Has some interesting features, like bubble plot, but seems to be stagnant. The CEO doesn't seem technical and they don't have strong AI talent on their team | ||
Pubmed Advanced Search | Actually somewhat powerful, but again only on the bottom-up semantics and not the top-down relation extraction level -probably because it's not deterministic and pubmed doesn't want to be responsible for noisy results | |
{{or: Semantic | Google}} Scholar | Google's UVP is "we have ALL papers". Semantic Scholar is pushing actually new features monthly now. Interesting, but only [[lowest common denominator apps]] that work for ALL scholars (horizontal AI tech; not optimized for specific workflows $$$). Features are focused on horizontally applicable ML (like [[discourse tagging]] and [[Recommender System]]) |