Open source effort to bring open biomedical data together – anyone interested?
inspiratron.org
inspiratron.org
The linked data/semantic web approaches are slowly eating away at the unneeded data diversity. With small shared standards for common data, and unique schemas for unique data. The single endpoint solution unfortunately does not scale; it would be the AOL of biomedical data or entrez as you prefer. To be truly open and free anyone should be able to contribute their data and tools. This means decentralized infrastructure which means confusion and difficult to find information. However, as acedemia and research is decentralized their IT infrastructure must match that reality. This leads to infrastructure that can integrate on demand, such as made possible by the SPARQL service keyword.
Open source has always been an important part of bio-IT and that is not going to change. But a single source is not the answer to our problems. We need to make it easier to find information, but most importantly need to make it easier to answer questions with the data that is available.
Would it be possible to make a compromise here? Perhaps by maintaining the decentralized structure, but at the same time introducing standards to categorize data on a global level and allow it to be 'mined' by a centralized entity that has API access to this data. Kind of like Google does indexing and searching, just with more cooperation of those being indexed.
As I've said I'm no expert, but I'd be interested to use a project like this to learn more about the field and to find ways to contribute. Atm, I can help setup the initial infrastructure for the project and cover the AWS costs for the first few months until you get some funding. Feel free to reach out if you think this could help, email is in my profile.
Everyone in biomedical research dreams about integrated resources. I have heard multiple people advocating SPARQL as well. If it had been that easy, this would have occurred years ago. In the real world, no one is even close. If you want to attract collaborators, learn Linus: say you have a working prototype and demonstrate how wonderful it is. Your ideas are cheap. The difficult part is a clear roadmap to make it happen.
Based on what I see happening in large orgs with lots of machine learning resources is the development of new techniques to generate large amounts of homogenous phenotypic data across many measurement modalities. These large orgs have biologist/doctors: the small number of people cross-trained well enough to move between the two fields with ease. These orgs have gathered enough resources to compel the leading researchers to work with them, and they're starting to publish interesting papers.
(I know very little about the issue, but this seems to be a problem in many fields of academia.)
One of the coarchitects of openphacts here. A pointer for developers is dev.openphacts.org. All the source and data is open.
While I agree that standards are hard I think the major issue is sustaining these things. You need some set of people to code and curate even if it's small and you need good uptime/support to gain community trust.
I work on the project but find it pretty uninspiring. It presents a dark image of the future in which a handful of large tech companies control all of our biomedical data and we have to beg them to allow us to share it. I guess that sounds pretty similar to the present. Just switch bio and social and here we are.
It's not literally CORBA, but people who spent time implementing literal CORBA in a bioinformatics context (for instance, the original author of https://github.com/bioperl/bioperl-corba-server) have noted that the design pattern and discussions followed by the GA4GH are pretty similar to those had in the EBI when they attempted to unify everything using CORBA.
> The data model is not implicit, it's explicit (there is a schema).
There is a schema, but the semantics of the data model are encoded in the comments of the schema. Without hooking into some kind of ontological basis it doesn't seem possible to avoid this.
> As for the semantic web, every bio project I've seen which adopted the semantic web ultimately failed -- the semantic web seems like a great idea, but attempts to fully implement to the point where it's useful for research always fail.
I'm aware of at least one group in the GA4GH that uses RDF internally, then converts into the custom schemas produced by the group in order to maintain compatibility with the top-down designs of the project. I believe this is the phenotype group. These are the people who are most interested what the author of the linked page is describing, they have decided to use the technology you believe is doomed to fail. But, they aren't failing. As far as I can tell from their presentations they are one of two or three groups in the project that have produced a functioning system.
It's very easy so say that hard things are impossible. This tends to keep them that way. I doubt we have any other viable option for building large distributed knowledge systems. The fact that these don't exist does not mean they are impossible to construct, but simply that no one has managed to do so yet. People leveled the same kinds of arguments against neural networks up until a few years ago, saying that they were a nice idea but destined to fail because they were too hard.
> So I think they're focusing on areas where they are likely to succeed (collection and processing of large amounts of raw and derived data using pretty conventional processes, but at a much larger scale, with a solid authentication and access mechanism).
The scales we're talking about are not even an order of magnitude above that which existing techniques allow. So I agree that they will succeed insofar as they simply adopt these existing community-driven standards and slap access control on top. However, in terms of generating new data models for genomics, I'm not so convinced that the centralized design and API-based approach which they are taking will work. I guess we will have to meet back here in a few years and see what happened.
I was initially quite impressed with the GA4GH effort, because it was transparent and producing useful things, in terms of schema and code in github repositories. I am afraid now it has all gone working groups and private email threads and very little is happening in the open any more.
I wish I understood semantic web. I've built more than one system on it but have never found a use case that sells it for me.
The whole idea of the semantic web is exactly what the author is getting at. I'm curious why this is rarely regarded as a serious basis for such an effort like that which the author is promoting.