Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge by Thierry Hanser, et al. (Journal of Cheminformatics 2014, 6:21)
Abstract:
Background
Combining different sources of knowledge to build improved structure activity relationship models is not easy owing to the variety of knowledge formats and the absence of a common framework to interoperate between learning techniques. Most of the current approaches address this problem by using consensus models that operate at the prediction level. We explore the possibility to directly combine these sources at the knowledge level, with the aim to harvest potentially increased synergy at an earlier stage. Our goal is to design a general methodology to facilitate knowledge discovery and produce accurate and interpretable models.
Results
To combine models at the knowledge level, we propose to decouple the learning phase from the knowledge application phase using a pivot representation (lingua franca) based on the concept of hypothesis. A hypothesis is a simple and interpretable knowledge unit. Regardless of its origin, knowledge is broken down into a collection of hypotheses. These hypotheses are subsequently organised into hierarchical network. This unification permits to combine different sources of knowledge into a common formalised framework. The approach allows us to create a synergistic system between different forms of knowledge and new algorithms can be applied to leverage this unified model. This first article focuses on the general principle of the Self Organising Hypothesis Network (SOHN) approach in the context of binary classification problems along with an illustrative application to the prediction of mutagenicity.
Conclusion
It is possible to represent knowledge in the unified form of a hypothesis network allowing interpretable predictions with performances comparable to mainstream machine learning techniques. This new approach offers the potential to combine knowledge from different sources into a common framework in which high level reasoning and meta-learning can be applied; these latter perspectives will be explored in future work.
One interesting feature of this publication is a graphic abstract:
Assuming one could control the length of the graphic abstracts, that would be an interesting feature for conference papers.
What should be the icon for repeating old news before getting to the new stuff? 😉
Among a number of good points in this paper, see in particular:
- Distinction between SOHN and “a Galois lattice used in Formal Concept
Analysis [19] (FCA)” (at page 10). - Discussion of the transparency of this approach at page 21.
In a very real sense, announcing an answer to a medical question may be welcome, but it isn’t very informative. Nor will it enable others to advance the medical arts.
Other domains where answers are important but how you arrived at an answer is equally important if not more so?