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Trying to squeeze sense out of chemical data

Archive for the ‘predictive modeling’ tag

Thoughts on the DREAM Synergy Prediction Challenge

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The DREAM consortium has run a number of predictive modeling challenges and the latest one on predicting small molecule synergies has just been published. The dataset that was provided included baseline gene expression of the cell line (OCI-LY3), expression in presence of compound (2 concentrations, 2 time points), dose response data for 14 compounds and the excess over Bliss for the 91 pairs formed from the 14 compounds. Based on this data (and available literature data) participants had to predict a ranking for the 91 combinations.

The paper reports the results of 31 approaches (plus one method that was not compared to the others) and does a good job of summarizing their performance and identifying whether certain data type or certain approaches work better than others. They also investigated the performance of an ensemble of approaches, which, as one might expect, worked better than the single methods. While the importance of gene expression in predictive performance was not as great as I would’ve thought, it was certainly more useful than chemical structure alone. Interestingly, they also noted that “compounds with more targeted mechanisms, such as rapamycin and blebbistatin, were least synergistic“. I suspect that this is somewhat dataset specific, but it will be interesting to see whether this holds in large collections of combination experiment such as those run at NCATS.

Overall, it’s an important contribution with the key take home message being

… synergy and antagonism are highly context specific and are thus not universal properties of the compounds’ chemical, structural or substrate information. As a result, predictive methods that account for the genetics and regulatory architecture of the context will become increasingly relevant to generalize results across multiple contexts

Given the relative dearth of predictive models of compound synergy, this paper is a nice compilation of methods. But there are some issues that weaken the paper.

  • One key issue are the conclusions on model performance. The organizers defined a score, termed probabilistic c-score (PC score). If I understand correctly, a random ranking should give PC = 0.5. It turns out that the best performing method exhibited a PC score = 0.61 with a number of methods hovering around 0.5. Undoubtably, this is a tough problem, but when the authors states that “… this challenge shows that current methodologies can perform significantly better than chance …” I raise an eyebrow. I can only assume that what they meant was that the results were “statistically significantly better than chance“, because in terms of effect size the results are not impressive. After reading this excellent article on p-values and significance testing I’m particularly sensitized to claims of significance.
  • The dataset could have been strengthened by the inclusion of self-crosses. This would’ve allowed the authors to assess actual excess over Bliss values corresponding to additivity (which will not be exactly 0 due to experimental noise), and avoid the use of cutoffs in determining what is synergistic or antagonistic.
  • Similarly, a key piece of data that would really strengthen these approaches is the expression data in presence of combinations. While it’s unreasonable to have this data available for all combinations, it could be used as a first step in developing models to predict the expression profile in presence of combination treatment. Certainly, such data could be used to validate some assumptions made by some of the models described (e.g., concordance of DEG’s induced by single agents implies synergistic response).
  • Kudos for including source code for the top methods, but would’ve been nicer if data files were included so we could actually reproduce the results.
  • The authors conclude that when designing new synergy experiments, one should identify mechanistically diverse molecules to make up for the “small number of potentially synergistic pathways“. While mechanistic diversity is a good idea, it’s not clear how they conclude there are a small number of pathways that play a role in synergy.
  • It’s a pity that the SynGen method was not compared to the other methods. While the authors provide a justification, it seems rather weak. The method only applied to the synergistic combinations (performance was not a whole lot better than random – true positive rate of 56%) – but the text indicates that it predicted synergistic compound pairs. It’s not clear whether this means it made a call on synergy or a predicted ranking. If the latter it would’ve been interesting to see how it compared to the rankings of the synergistic subset of 91 compounds from other methods.

Written by Rajarshi Guha

November 20th, 2014 at 5:37 pm

Predictive models – Implementation vs Specification

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Benjamin Good recently asked about the existence of public repositories of predictive molecular signatures. From his description, he’s looking for platforms that are capable of deploying predictive models. The need for something like this is certainly not restricted to genomics – the QSAR field has been in need for this for many years. A few years back I described a system to deploy R models and more recently the OCHEM platform attempts to address this. Pipelining tools usually have a web deployment mode that also supports this idea. One problem faced by such platforms in the cheminformatics area is that the deployed model must include the means to evaluate the input features (a.k.a., descriptors). Depending on the licenses associated with descriptor software such a bundle may not be easily deployed. A gene-based predictor obviously doesn’t suffer from this problem, so it should be easier to implement. Benjamin points out the Synapse platform which looks quite nice, but only supports R models (not necessarily a bad thing!). A very recent candidate for generic predictive model (amongst other things) deployment is via plugins for the BARD platform.

But in my mind, the deeper issue that should be addressed is that of model specification. With a robust specification, evaluation of the model could implemented in arbitrary languages and platforms – essentially decoupling model definition and model implementation. PMML is one approach to predictive model specifications and is quite general (and a good solution for the gene predictor models that Benjamin is interested in). A field-specific example would be QSAR-ML (also see here) for QSAR models. One could then imagine repositories of model specifications, with an ecosystem of tools and services that instantiate models from these specs.

Written by Rajarshi Guha

May 1st, 2013 at 12:29 am