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Uniprot fills a pretty big niche - proteins. Your body is literally made out of proteins. They carry out most of the work and chemical reactions that amount t

Uniprot fills a pretty big niche - proteins. Your body is literally made out of proteins. They carry out most of the work and chemical reactions that amount to what we call life, and almost all drugs work by modulating some protein target in some way. UniprotIDs are the canonical identifiers that computational biologists use for proteins. But they are just one knowledge base. There are others. Each with their own focus area. And they are all associated with prominent bioNLP / biomedical AI research labs and employ human SME curators. > 5-10 avg salary non-star ML FTEs likely produce very mediocre and likely non usable results LOL!

riku_iki 3y ago
> But they are just one knowledge base. There are others. So, is your claim that all topics/niches/verticals/steps in pharma development and productions are covered by all those databases with absolute quality and perfect UI/workflow? I follow some studies about trials meta-analysis, and my impression is that end result is mainly produced by manual work of some low paid postdocs, which makes them not trustworthy. > LOL! I actually claim non-trivial expertise in this area (facts extraction from untrivial niche documents), and my observations is that even SOTA (aka results from top researchers) are hardly useable in real life, because this area is very hard. Could you support your "LOL!" with any references I could check?..
- MichaelZuo 3y ago
  The parent is right, saying a database about proteins 'looks like one narrow niche knowledge base.' in this context is just nonsense.
  - riku_iki 3y ago
    in which context, and why your bold statements should be trusted blindly?
    - deleted 3y ago
      [deleted]
    - Kim_Bruning 3y ago
      TL;DR: I explain what proteins are and why they're important, show some biological "flowcharts" , and end up with one "function definition" from the "source code" that makes you you, and has to do with you eating and breathing. Long: This is the bit that makes biology awesome to me, so excuse me for the small essay ;-) Proteins are basically extremely advanced nanomachines which work together in larger systems to ultimately form a cell. Having a listing of all the proteins in a cell (the sum of the parts) is insufficient to grok the whole, but it's pretty darn important. The abilities and limitations determine and constrain what a cell can do, and ultimately influence what organisms and ecosystems are and are not capable of. Which is a big chunk of the science of biology. Uniprot lists many/all of the genes/proteins that have been decoded so far. It's a bit odd to call that a "niche" in the context of the field of biology. I'm not going to discourage you though: Proteins and protein systems are pretty darn awesome! Example: (using KEGG rather than uniprot, since it's got graphical maps, which is handy to get an intuition) For instance, if you want to know why you need to eat and why you need to breathe (flowchart for what your cells do with starch and oxygen): * https://www.genome.jp/pathway/map00500 https://www.genome.jp/pathway/map00500 start by finding starch on this map (gets split into glucose) * https://www.genome.jp/pathway/map00010 https://www.genome.jp/pathway/map00010 which gets broken down into 2* pyruvate * https://www.genome.jp/pathway/map00020 https://www.genome.jp/pathway/map00020 which gets processed * https://www.genome.jp/pathway/map00190 https://www.genome.jp/pathway/map00190 and ultimately "burned" with oxygen. Each step is a 'chemical reaction catalyzed by proteins'[1] (in the rectangles). You can dig in deeper to find your actual source code: Say we click on a random step (in this case near the top of glycolysis on map 00010) * https://www.genome.jp/entry/K01810+K06859+K13810+K15916+5.3.1.9+R13199 https://www.genome.jp/entry/K01810+K06859+K13810+K15916+5.3.... At the bottom you can find the gene listed for Homo Sapiens (HSA) * https://www.genome.jp/entry/hsa:2821 https://www.genome.jp/entry/hsa:2821 And this lists the amino-acid (AA) sequence for the protein, and the nucleotide (NT) sequence found in humans. Since this is highly preserved functionality, that's probably (almost) exactly the source code that you have in each of your cells. KEGG is nice to get an overview of some of the pathways that are fully understood with the maps. [1] calling it a "chemical reaction" is sort of underselling many proteins. Proteins can have moving parts and can work together. I prefer to think of them as sophisticated nanomachines.
- pfisherman 3y ago
  I already gave you a real life example with the uniprot reference. Here is another flagship knowledge base that heavily leverages NLP extraction.[0] Here is another one that gets used in what seems like every network biology article.[1] Meta analyses? Automating meta analyses is not a real need. They have their place, but it’s like a quaint cottage industry type thing - like custom haberdashery. Also the most valuable knowledge is not in any publication. If you are reading about it in an article then you are already 2-3 years too late. 0. https://geneontology.org https://geneontology.org 1. https://string-db.org/ https://string-db.org/
  - riku_iki 3y ago
    > that heavily leverages NLP extraction I search https://www.google.com/search?q=site%3Ageneontology.org+nlp+extraction https://www.google.com/search?q=site%3Ageneontology.org+nlp+... and don't see anything meaningful. > Automating meta analyses is not a real need. They have their place, but it’s like a quaint cottage industry type thing - like custom haberdashery. my opinion is that it has to be top level tool in modern science which could easily sort out lots of bs and contradictions in reported results.

riku_iki 3y ago

> But they are just one knowledge base. There are others. So, is your claim that all topics/niches/verticals/steps in pharma development and productions are covered by all those databases with absolute quality and perfect UI/workflow? I follow some studies about trials meta-analysis, and my impression is that end result is mainly produced by manual work of some low paid postdocs, which makes them not trustworthy. > LOL! I actually claim non-trivial expertise in this area (facts extraction from untrivial niche documents), and my observations is that even SOTA (aka results from top researchers) are hardly useable in real life, because this area is very hard. Could you support your "LOL!" with any references I could check?..

MichaelZuo 3y ago
The parent is right, saying a database about proteins 'looks like one narrow niche knowledge base.' in this context is just nonsense.
- riku_iki 3y ago
  in which context, and why your bold statements should be trusted blindly?
  - deleted 3y ago
    [deleted]
  - Kim_Bruning 3y ago
    TL;DR: I explain what proteins are and why they're important, show some biological "flowcharts" , and end up with one "function definition" from the "source code" that makes you you, and has to do with you eating and breathing. Long: This is the bit that makes biology awesome to me, so excuse me for the small essay ;-) Proteins are basically extremely advanced nanomachines which work together in larger systems to ultimately form a cell. Having a listing of all the proteins in a cell (the sum of the parts) is insufficient to grok the whole, but it's pretty darn important. The abilities and limitations determine and constrain what a cell can do, and ultimately influence what organisms and ecosystems are and are not capable of. Which is a big chunk of the science of biology. Uniprot lists many/all of the genes/proteins that have been decoded so far. It's a bit odd to call that a "niche" in the context of the field of biology. I'm not going to discourage you though: Proteins and protein systems are pretty darn awesome! Example: (using KEGG rather than uniprot, since it's got graphical maps, which is handy to get an intuition) For instance, if you want to know why you need to eat and why you need to breathe (flowchart for what your cells do with starch and oxygen): * https://www.genome.jp/pathway/map00500 https://www.genome.jp/pathway/map00500 start by finding starch on this map (gets split into glucose) * https://www.genome.jp/pathway/map00010 https://www.genome.jp/pathway/map00010 which gets broken down into 2* pyruvate * https://www.genome.jp/pathway/map00020 https://www.genome.jp/pathway/map00020 which gets processed * https://www.genome.jp/pathway/map00190 https://www.genome.jp/pathway/map00190 and ultimately "burned" with oxygen. Each step is a 'chemical reaction catalyzed by proteins'[1] (in the rectangles). You can dig in deeper to find your actual source code: Say we click on a random step (in this case near the top of glycolysis on map 00010) * https://www.genome.jp/entry/K01810+K06859+K13810+K15916+5.3.1.9+R13199 https://www.genome.jp/entry/K01810+K06859+K13810+K15916+5.3.... At the bottom you can find the gene listed for Homo Sapiens (HSA) * https://www.genome.jp/entry/hsa:2821 https://www.genome.jp/entry/hsa:2821 And this lists the amino-acid (AA) sequence for the protein, and the nucleotide (NT) sequence found in humans. Since this is highly preserved functionality, that's probably (almost) exactly the source code that you have in each of your cells. KEGG is nice to get an overview of some of the pathways that are fully understood with the maps. [1] calling it a "chemical reaction" is sort of underselling many proteins. Proteins can have moving parts and can work together. I prefer to think of them as sophisticated nanomachines.
pfisherman 3y ago
I already gave you a real life example with the uniprot reference. Here is another flagship knowledge base that heavily leverages NLP extraction.[0] Here is another one that gets used in what seems like every network biology article.[1] Meta analyses? Automating meta analyses is not a real need. They have their place, but it’s like a quaint cottage industry type thing - like custom haberdashery. Also the most valuable knowledge is not in any publication. If you are reading about it in an article then you are already 2-3 years too late. 0. https://geneontology.org https://geneontology.org 1. https://string-db.org/ https://string-db.org/
- riku_iki 3y ago
  > that heavily leverages NLP extraction I search https://www.google.com/search?q=site%3Ageneontology.org+nlp+extraction https://www.google.com/search?q=site%3Ageneontology.org+nlp+... and don't see anything meaningful. > Automating meta analyses is not a real need. They have their place, but it’s like a quaint cottage industry type thing - like custom haberdashery. my opinion is that it has to be top level tool in modern science which could easily sort out lots of bs and contradictions in reported results.