5 ms·

it is one of these misguided efforts to bring some order to life sciences but they go about it the wrong way. It is so heavy-handed, the website so obtuse and

it is one of these misguided efforts to bring some order to life sciences but they go about it the wrong way. It is so heavy-handed, the website so obtuse and confusing that no life scientist I know (and I have worked with hundreds of them) is even aware of let alone understand what an ontology is or how to use it. Might be hard to believe for the uninitiated but they even got some of the namings is wrong from the start, for example, they have a GO (Gene Ontology) but that ontology is not actually describing genes! It describes gene products (like proteins) a huge big difference! Not to mention grossly misleading the very life scientists it is meant to help.

xipho 7y ago
Curious, coming from a user named 'glofish' that they are unaware of GOs pervasiveness in the zebrafish model organsim world. GO is everywhere in the genomics/model organism world, and only growing in use. Also funny that the user is looking at labels, when concepts are the core of an ontology. Take a look at how many terms in GO are deprecated, it has evolved over time, I know very few scientists who get things right the first time. Sure there are issues, but many FOSS principles apply throughout. Also, NIH would be to differ that OWL/OBO isn't important- https://monarchinitiative.org/ https://monarchinitiative.org/.
- netfl0 7y ago
  Excellent reply. The GO is very impressive and I don’t think most folks have seen it.
- glofish 7y ago
  People do not understand and misuse GO. They think it is Gene-Ontology - it is not. It is gene product ontology. In addition, I guarantee you that the vast majority of people using GO understand neither: - what the ontology actually is - how it works - who decides what gets into the data - why is something labeled a certain way - what evidence is there - how the terms interconnect - what the hierarchy all means All that because the concepts are not explained properly, nor the site is of any use to help you figure these out. It is mostly an illusion - and I am saying that as someone that uses GO a lot. I am intimately familiar with all of its pitfalls. At best some people know is that a label is attached to a gene. Finally GO is also perhaps the odd one out, the only ontology that is known somewhat because it is misused a lot. I invite you to go to the link on the top post and note how many other ontologies are there ... hundreds? Ask a life scientist how many they have heard of. Is my original statement all that wrong really? I don't think so. These ontologies are dead-end.
  - xipho 7y ago
    From in-depth experience I completely concur with your bullet points, they are all valid (spot on really), and in some cases huge blockers for larger scale adoption. I've had plenty of existential angst regarding the amount of time I've invested in the ontology world. Navigating, inferring, understanding, all the things necessary to make the use of ontologies, need a lot of work, particularly new interfaces (but- hmm, sounds like Science). IMO, however, these issues don't invalidate the underlying effort or goals. Biology is vast, and difficult. In my view these ontologies are focal points that force biologists to think about what they are doing, this, by itself, is enough, to me, everything else is bonus. I've seen communities of biologists come to a new awareness as to how bad their existing scientific-terminology is when they go through ontology-building exercises. Scientists often use terms they think they know the meaning of because their academic ancestors all used those terms. Simply having scientists work through these issues is of value (again, Science == Slow). Good luck with using AI to understand human labels, you're going to need more structure (formalized scientific consensus). Ontologies are one way to contribute to this structure/consensus. Of course they, like every other knowledge-base, are not a stand-alone answer. This leaves me with: "I am saying that as someone that uses GO a lot."- but why!? Since you're following this "dead-end" I suspect you're not a scientist, but rather someone selling something, and as such you have no problems using a tool to make a $, even knowing it's pointless in the long run?
    - glofish 7y ago
      I use it because other scientists make me do it :-) The other sad reality is that this "dead-end" is currently the best we got in science. Sigh.
      - xipho 7y ago
        Fair enough. Curious if you have a gut feeling for what alternatives are?
deleted 7y ago
[deleted]
ruarai 7y ago
Working on genomics stuff right now and gene ontology (GO) is ubiquitous. My primary complaint with GO is that it doesn't map to any statistical analyses very well
- chrismungall 7y ago
  Can you expand on that? One of the main use cases for GO is statistical enrichment analyses. Of course, there are definitely problems here, from choice of appropriate way to correct for multiple hypotheses to appropriateness of frequentist vs Bayesian methods (e.g. Ontologizer). Is this what you are getting at?
- boublepop 7y ago
  How do you work with it? Are you writing scripts and sparqQL that query an endpoint, or ser you using an ontologi aware databrowser that can do calculations? Or something completely different?

xipho 7y ago

Curious, coming from a user named 'glofish' that they are unaware of GOs pervasiveness in the zebrafish model organsim world. GO is everywhere in the genomics/model organism world, and only growing in use. Also funny that the user is looking at labels, when concepts are the core of an ontology. Take a look at how many terms in GO are deprecated, it has evolved over time, I know very few scientists who get things right the first time. Sure there are issues, but many FOSS principles apply throughout. Also, NIH would be to differ that OWL/OBO isn't important- https://monarchinitiative.org/ https://monarchinitiative.org/.

netfl0 7y ago
Excellent reply. The GO is very impressive and I don’t think most folks have seen it.
glofish 7y ago
People do not understand and misuse GO. They think it is Gene-Ontology - it is not. It is gene product ontology. In addition, I guarantee you that the vast majority of people using GO understand neither: - what the ontology actually is - how it works - who decides what gets into the data - why is something labeled a certain way - what evidence is there - how the terms interconnect - what the hierarchy all means All that because the concepts are not explained properly, nor the site is of any use to help you figure these out. It is mostly an illusion - and I am saying that as someone that uses GO a lot. I am intimately familiar with all of its pitfalls. At best some people know is that a label is attached to a gene. Finally GO is also perhaps the odd one out, the only ontology that is known somewhat because it is misused a lot. I invite you to go to the link on the top post and note how many other ontologies are there ... hundreds? Ask a life scientist how many they have heard of. Is my original statement all that wrong really? I don't think so. These ontologies are dead-end.
- xipho 7y ago
  From in-depth experience I completely concur with your bullet points, they are all valid (spot on really), and in some cases huge blockers for larger scale adoption. I've had plenty of existential angst regarding the amount of time I've invested in the ontology world. Navigating, inferring, understanding, all the things necessary to make the use of ontologies, need a lot of work, particularly new interfaces (but- hmm, sounds like Science). IMO, however, these issues don't invalidate the underlying effort or goals. Biology is vast, and difficult. In my view these ontologies are focal points that force biologists to think about what they are doing, this, by itself, is enough, to me, everything else is bonus. I've seen communities of biologists come to a new awareness as to how bad their existing scientific-terminology is when they go through ontology-building exercises. Scientists often use terms they think they know the meaning of because their academic ancestors all used those terms. Simply having scientists work through these issues is of value (again, Science == Slow). Good luck with using AI to understand human labels, you're going to need more structure (formalized scientific consensus). Ontologies are one way to contribute to this structure/consensus. Of course they, like every other knowledge-base, are not a stand-alone answer. This leaves me with: "I am saying that as someone that uses GO a lot."- but why!? Since you're following this "dead-end" I suspect you're not a scientist, but rather someone selling something, and as such you have no problems using a tool to make a $, even knowing it's pointless in the long run?
  - glofish 7y ago
    I use it because other scientists make me do it :-) The other sad reality is that this "dead-end" is currently the best we got in science. Sigh.
    - xipho 7y ago
      Fair enough. Curious if you have a gut feeling for what alternatives are?

deleted 7y ago

[deleted]

ruarai 7y ago

Working on genomics stuff right now and gene ontology (GO) is ubiquitous. My primary complaint with GO is that it doesn't map to any statistical analyses very well

chrismungall 7y ago
Can you expand on that? One of the main use cases for GO is statistical enrichment analyses. Of course, there are definitely problems here, from choice of appropriate way to correct for multiple hypotheses to appropriateness of frequentist vs Bayesian methods (e.g. Ontologizer). Is this what you are getting at?
boublepop 7y ago
How do you work with it? Are you writing scripts and sparqQL that query an endpoint, or ser you using an ontologi aware databrowser that can do calculations? Or something completely different?