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Notes & thoughts from the IU semantics workshop

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Over the last two days I attended a workshop titled Exploiting Big Data Semantics for Translational Medicine, held at Indiana University, organized by David Wild, Ying Ding, Katy Borner and Eric Gifford. The stated goals were to explore advances in translation medicine via data and semantic technologies, with a view towards possible fundable ideas and funding opportunites. A nicely arranged workshop that was pretty intense – minimal breaks, constant thinking – which is a good use of 2 days. As you can see from the workshop website, the attendees brought a variety of skills and outlooks to the meeting. For me this was one of the most attractive features of the workshop.

This post is a rough dump of some observations & thoughts during the workshop – I’m sure I’ve left out important comments, provide minimal attribution and I assume there will be a more thorough report coming out from the organizers. I also point out that I am an interested bystander to this field and somewhat of a semantic web/technology (SW/T) skeptic – so some views may be naive or just wrong. I like the ideas and concepts, I can see their value, but I have not been convinced to invest significant time and efort into “semantifying” my day to day work. A major motivation for my attending this workshop was to learn what the experts are doing and see how I could incorporate some of these ideas into my own work.

The Meeting

The first day started with 5 minute introductions which was quite useful and great overview talks by three of the attendees. With the information dump, a major focus of the day was a discussion of opportunities and challenges. This was a very useful session with attendees listing specific instances of challenges, opportunities, bottlenecks and so on. I was able to take some notes on the challenges, including

  • Funding – lack of it and difficulty in obtaining it (i.e., persuading funders)
  • Cultural and social issues around semantic approaches (e.g., why change what’s already working? etc)
  • Data problems such as errors being propagated through ontologies and semantic conversion processes etc (I wonder to what extent this is a result of automated conversion processes such as D2R, versus manual errors introduced during curation. I suspect a mix of both)
  • Hilbert Problems” – a very nice term coined by Katy to represent grand challenges or open problems that could serve as seeds around which the community could nucleate. (This aspect was of particular interest as I have found it difficult to identify compelling life science use cases that justify a retooling (even partial) of current workflows.)

The second day focused on breakout sessions, based on the opportunities and challenges listed the day before. Some notes on some of these sessions:

Bridging molecular data and clinical data – this session focused on challenges and opportunities in using molecular data together with clinical data to inform clinical decision making. Three broad opportunies came out of this, viz., Advancing understanding of disease conditions, Optimizing data types/measurements for clinical decision making outcomes and Drug repurposing. Certainly very broad goals, and not particularly focused on SW/T. My impression that SW/T can play an important role in standardization and optimization of coding standards to more easily and robustly connect molecular and clinical data sources. But one certainly needn’t invoke SW/T to address these opportunities

Knowledge discovery – the considerations addressed by this group included the fact that semantified data (vocabularies, ontologies etc) is increasing in volume and availability, tools are available to go from raw data to semantified forms and so on. An important point was made the quality is a key consideration at multiple levels – the raw data, the semantic representation and the links between semantic entities. A challenge identified by this group was to identify use cases that SW/T can resolve and traditional technologies cannot.

RDBMS vs semantic databases – this was an interesting session that tried to address the question of when one type of database is better than the other. It seems that the consensus was that certain problems are better suited for one type over another and a hybrid solution is usually a sensible approach – but that goes without saying. A comment was made that certain classes of problems that involve identifying paths between terms (nodes) are better suited for semantic (graph) databases – this makes intuitive sense, but there was a consensus that there weren’t any realistic applications that one could point to. I like the idea – have attributes in a RDBMS, but links in a graph database and use graph queries to identify relations and entities that are then mapped to the RDBMS. My concern with this is that path traversals are easy (Neo4J does this quite efficiently) – the problem is in the explosion of possible paths between nodes and the fact that the majority of them are trivial at best or nonsensical at worst. This suggests that relevance/ranking is a concern in semantic/graph databases.

The session of most interest to me was that of grand challenges. I think we got to 5  or 6 major challenges

  • How to represent knowledge (methods for, evaluation of)
  • How do changes in ontologies affect scientific research over time
  • How to construct an ontology from a set of ontologies (i.e. preexisting knowledge) that is better than the individual ones (and so links to how to evaluate an ontology in terms of “goodness”)
  • Error propagation from measurements to representation to analysis
  • Visualization of multi-dimensional / high dimensional data – while a general challenge, I think it’s correct that visual representations of semantified data (and their supporting infrastructure such as ontologies) could make the methods and tools much more accessible. Would’ve been nice if we had more discussion on this aspect

We finally ended with a discussion concrete projects that attendees would be interested in collaborating on and this was quite fruitful.

My Opinion

It turns out that a good chunk of the discussion focused on translational medicine (clinical informatics, drug repurposing etc.) and the use of different data types to enable life science research, but largely independent of SW/T. Indeed, the role of SW/T seemed rather fuzzy at times – to some extent, a useful tool, but not indispensable. My impression was that much of the SW/T that was discussed really focused on labeling of knowledge via ontologies and making links between datasets and the challenges faced during these operations (which is fine and important – but does it justify funding?).

I certainly got some conflicting views of the state of the art. Comments from Amit Sheth made it appear that SW/T is well established and the main problems are solved, based on deployed applications in the “enterprise”. But comments from many of the attendes working in life sciences suggested many problems in dealing and working with semantic data. Sure, Google has it’s Knowledge Graph and other search engines are employing SW/T under the hood. But if it’s so well established, where are the products, tools and workflows that an informatics-savvy non-expert in SW/T can employ? Does this mean research funding is not really required and it’s more of a productization/monetization issue? Or is this a domain specific issue – what works for general search doesn’t necessarily work in the life sciences?

My fundamental issue is the absence of a “killer application” – an application or use case that gives a non-trivial result, that could not be achieved via traditional means. (I qualify this, by asking for such use cases in life sciences. Maybe bankers have already found their killer applications). Depending on the semantic technology one considers there are partial answers: ontologies are an example of such a use case, when used to enable linkages between datasets and sources across domains. To me this makes perfect sense (and is of particular interest and use in current projects such as BARD). But surely, there must be more than designing ontologies and annotating data with ontological terms? One of the things that was surprising to me was that some of the future problems that were considered for possible collaborations were not really dependent on SW/T – in other words, they could largely be addressed via pre-existing methodologies.

My (admittedly cursory) reading of the SW/T literature seems to suggest to me that a major promise of this field is “reasoning” over my data. And I’m waiting for non-trivial assertions made based on linked data, ontologies and so on – that really highlight where my SQL tables will fail. It’s not sufficient (to me) to say that what took me 50 lines of Python code takes you 2 lines of SPARQL – I have an investment made in my RDBMS, API’s and codebase and yes, it takes a bit more fiddling – but I can get my answer in 5 minutes because it’s already been set up.

Some points were made regarding challenges faced by SW/T including complexity of OWL, difficulty in leaning SPARQL, poor performanec queries. Personally, these are not valid challenges and I certainlly do not make the claim that tricky SPARQL queries are preventing me from jumping into SW/T. I’m perfectly willing to wait 5 min for a SPARQL query to run, if the outcome is of sufficient value. The bigger issue for me is the value of the outcomes – maybe it’s just too early for truly novel, transformative results to be produced. Or maybe it’s simply one tool amongst others that can be used to tackle a certain class of problems.

Overall, it was a worthwhile two days interacting with a group of interesting people. But definitely some fuzziness in terms of what role SW/T can, should or will play in translational life science research.

Written by Rajarshi Guha

March 27th, 2013 at 7:26 pm

Competitive Predictive Modeling – How Useful is it?

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While at the ACS National Meeting in Philadelphia I attended a talk by David Thompson of Boehringer Ingelheim (BI), where he spoke about a recent competition BI sponsored on Kaggle – a web site that hosts data mining competitions. In this instance, BI provided a dataset that contained only object identifiers and about 1700 numerical features and a binary dependent variable. The contest was open to anybody and who ever got the best classification model (as measured by log loss) was selected as the winner. You can read more about the details of the competition and also on Davids’ slides.

But I’m curious about the utility of such a competition. During the competition, all contestents had access to were the numerical features. So the contestants had no idea of the domain from where the data came – placing the onus on pure modeling ability and no need for domain knowledge. But in fact the dataset provided to them, as announced by David at the ACS, was the Hansen AMES mutagenicity dataset characterized using a collection of 2D descriptors (continuous topological descriptors as well as binary fingerprints).

BI included some “default” models and the winning models certainly performed better (10% for the winning model). This is not surprising, as they did not attempt build optimized models. But then we also see that the top 5 models differed only incrementally in their log loss values. Thus any one of the top 3 or 4 models could be regarded as a winner in terms of actual predictions.

What I’d really like to know is how well such an approach leads to better chemistry or biology. First, it’s clear that such an approach leads to the optimization of pure predictive performance and cannot provide insight into why the model makes an active or inactive call. In many scenario’s this is sufficient, but more often than not, domain specific diagnostics are invaluable. Second, how does the relative increase in model performance lead to better decision making? Granted, the crowd-sourced, gamified approach is a nice way to eke out the last bits of predictive performance on a dataset – but does it really matter that one model performs 1% better than the next best model? The fact that the winning model was 10% better than the “default” BI model is not too informative. So a specific qustion I have is, was there a benefit, in terms of model performance, and downstream decision making by asking the crowd for a better model, compared to what BI had developed using (implicit or explicit) chemical knowledge?

My motivation is to try and understand whether the winning model was an incremental improvement or whether it was a significant jump, not just in terms of numerical performance, but in terms of the predicted chemistry/biology. People have been making noises of how data trumps knowledge (or rather hypotheses and models) and I believe that in some cases this can be true. But I also wonder to what extent this holds for chemical data mining.

But it’s equally important to understand what such a model is to be used for. In a virtual screening scenario, one could probably ignore interpretability and go for pure predictive performance. In such cases, for increasingly large libraries, it might make sense for one to have a model that s 1% better than the state of the art. (In fact, there was a very interesting talk by Nigel Duffy of Numerate, where he spoke about a closed form, analytical expression for the hit rate in a virtual screen, which indicates that for improvements in the overall performance of a VS workflow, the best investment is to increase the accuracy of the predictive model. Indeed, his results seem to indicate that even incremental improvements in model accuracy lead to a decent boost to the hit rate).

I want to stress that I’m not claiming that BI (or any other organization involved in this type of activity) has the absolute best models and that nobody can do better. I firmly believe that however good you are at something, there’s likely to be someone better at it (after all, there are 6 billion people in the world). But I’d also like to know how and whether incrementally better models do when put to the test of real, prospective predictions.

Written by Rajarshi Guha

August 22nd, 2012 at 9:02 pm

Software for the “Federation of Independent Scientists”

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A few days back, Derek Lowe posted a comment from a reader who suggested a way to approach the current employment challenges in the pharmaceutical industry would be the formation of a Federation of Independent Scientists. Such a federation would be open to consultants, small companies etc and would use its size to obtain group rates on various things – journal access, health insurance and so on. Obviously, there’s a lot of details left out here and when you go in the nitty gritty a lot of issues arise that don’t have simple answers. Nevertheless, an interesting (and welcome, as evidenced by the comment thread) idea.

One aspect raised by a commenter was access to modeling and docking software by such a group. He mentioned that he’d

… like to see an open source initiative develop a free, open source drug discovery package.Why not, all the underlying force fields and QM models have been published … it would just take a team of dedicated programmers and computational chemists time and passion to create it.

This is the very essence of the Blue Obelisk movement, under whose umbrella there is now a wide variety of computation chemistry and cheminformatics software. There’s certainly no lack of passion in the Open Source chemistry software community. As most of it is based on volunteer effort, time is always an issue. This has a direct effect on the features provided by Open Source chemistry software – such software does not always match up to commercial tools. But as the commenter above pointed out, much of the algorithms underlying proprietrary software is published. It just needs somebody with the time and expertise to implement them. And the combination of these two (in the absence of funding) is not always easy to find.

Of course, having access to the software is just one step. A scientists requires (possibly significant) hardware resources to run the software. Another comment raised this issue and asked about the possibility of a cloud based install of comp chem software.

With regards the sophisticated modelling tools – do they have to be locally installed?

How do the big pharma companies deploy the software now? I would be very suprised if it wasn’t easily packaged, although I guess the number of people using it is limited.

I’m thinking of some kind of virtual server, or remote desktop style operation. Your individual contractor can connect from whereever, and have full access to a range of tools, then transfer their data back to their own location for safekeeping.

Unlike CloudBioLinux, which provides a collection of bioinformatics and structural biology software as a prepackaged AMI for Amazons EC2 platform, I’m not aware of a similarly prepackaged set of Open Source tools for chemistry. And certainly not based on the cloud. (There are some companies that host comp chem software on the cloud and provide access to these installations for a fee). While some Linux distribibutions do package a number of scientific packages (UbuntuScience for example), I don’t think that these would support a computational drug discovery operation. (The above comment does’nt necessarily focus just on Open Source software. One could consider commercial software hosted on remote servers, though I wonder what type of licensing would be involved).

The last component would be the issue of data, primarily for cloud based solutions. While compute cycles on such platforms are usually cheap, bandwidth can be expensive. Granted, chemical data is not as big as biological data (cf. 1000Genomes on AWS), but sending a large collection of conformers over the network may not be very cost-effective. One way to bypass this would be to generate “standard” conformer collections and other such libraries and host them on the cloud. But what is “standard” and who would pay for hosting costs is an open question.

But I do think there is a sufficiently rich ecosystem of Open Source software that could serve much of the computational needs of a “Federation of Independent Scientists”. It’d be interesting to put together a list of Open Source based on requirements from the the commenters in that thread.

Written by Rajarshi Guha

April 14th, 2012 at 9:23 pm

I’d Rather Be … Reverse Engineering

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Gamification is a hot topic and companies such as Tunedit and Kaggle are succesfully hosting a variety of data mining competitions. These competitions employ data from a variety of domains such as bond trading, essay scoring and so on. Recently, both platforms have hosted a QSAR challenge (though not officially denoted as such). The most recent one is the challenge hosted at Kaggle by Boehringer Ingelheim.

While it’s good to see these competitions raise the profile of “data science” (and make some money for the winners), I must admit that these are not particularly interesting to me as it really boils down to looking at numbers with no context (aka domain knowledge). For example, in the Kaggle & BI example, there are 1,776 descriptors that have been normalized but no indication of the chemistry or biology. One could ask whether a certain mechanism of action is known to play a role in the biology being tested which could suggest a certain class of descriptors over another. Alternatively, one could ask whether there are a few distinct chemotypes present thus suggesting multiple local models versus a single global model. (I suppose that the supplied descriptors may lend themselves to a clustering, but a scaffold based approach would be much more direct and chemically intuitive).

This is not to say that such competitions are useless. On the contrary, lack of domain knowledge doesn’t preclude one from apply sophisticated statistical and machine learning methods to unannotated data and obtaining impressive results. The issue of data versus domain knowledge has been discussed in several places.

In contrast to the currently hosted challenge at Kaggle, an interesting twist would be to try and reverse engineer the structures from their descriptor values. There have been some previous discussions on reverse engineering structures from descriptor data. Obviously, we’re not going to be able to verify our results, but it would be an interesting challenge.

Written by Rajarshi Guha

April 6th, 2012 at 4:16 am

An ACS in (not so) Sunny San Diego

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Another ACS National meeting is over, this time in San Diego. It was good to catch up with old friends and meet many new, interesting people. As I was there for a relatively short period, I bounced around most sessions.

MEDI and COMP had a joint session on desktop modeling and its utility in medicinal chemistry. Anthony Nicholls gave an excellent talk, where he differentiated between “strong signals” and “weak signals”, the former being extremely obvious trends, features or facts that do not require a high degree of specialized exerptise to detect and the latter being those that do require significantly more expertise to identify. An example of a strong signal would be an empty region of a binding pocket that is not occupied by a ligand feature – it’s pretty easy to spot this and when hihglighted the possible actions are also obvious. A weak signal could be a pi-stacking interaction which could be difficult to identify in a crowded 3D diagram. He then highlighted how simple modifications to traditional 2D depictions can be used to make the obvious more obvious and make features that might be subtle, say in 3D, more obvious in a 2D depiction. Overall, an elegant talk, that focused on how simple visual cues in 2D & pseudo-3D depictions can key the mind to focus on important elements.

There were two other symposia that were of particular interest. On Sunday Shuxing Zhang and Sean Eakins organized a symposium on polypharmacology with an excellent line up of speakers including Chris Lipinski. Curt Breneman gave a nice talk that highlighted best practices in QSAR modeling and Marti Head gave a great talk on the role and value of docking in computational modeling projects.

On Tuesday, Jan Kuras and Tudor Oprea organized a session on System Chemical Biology. Though the session appeared to be more on the lines of drug repurposing, there were several interestingtalks. Ebelebola May from Sandia Labs gave a very interesting talk on a system level model of small molecule inhibition of M. Tuberculosis and F. Tularensis - combining metabolic pathway models and cheminformatics.

John Overington gave a very interesting talk on identifying drug combinations to improve safety. Contrary to much of my reading in this area, he points out the value of “me-too” drugs and taking combinations of such drugs. Given that such drugs hit the same target, he pointed out that this results in the fact that off-targets will see reduced concentrations of the individual drugs (hopefully reducing side effects) while the on-target will see the pooled concentration (thus maintaining efficacy (?)). It’s definitely a contrasting view to the one where we identify combinations of drugs hitting different targets (which I’d guess is a tougher proposition, since identifying a truly synergistic combination requires a detailed knowledge of the underlying pathways and interactions). He also pointed out that his analyses indicated that combination dosing is not actually reduced, in contrast to the current dogma.

As before we had a CINFlash session which I think went quite well – 8 diverse speakers with a pretty good audience. The slides of the talks have been made available and we plan to have another session in Philadelphia this Fall, so consider submitting something. We also had a great Scholarships for Scientific Excellence poster session – 15 posters covering topics ranging from reaction prediction to an analysis of retractions. Excellent work, and very encouraging to see newcomers to CINF interested in getting more invovled.

The only downsides to the meeting was the chilly and unsunny weather and the fact that people still think that displaying tables of numbers in a slide actually transmits any information!

Written by Rajarshi Guha

April 6th, 2012 at 2:39 am

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