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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/m
by 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 agoExcellent reply. The GO is very impressive and I don’t think most folks have seen it.
- glofish 7y agoPeople 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 agoFrom 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?