Archive for the ‘Self-Organizing’ Category

Self organising hypothesis networks

Saturday, May 10th, 2014

Self organising hypothesis networks: a new approach for representing and structuring SAR knowledge by Thierry Hanser, et al. (Journal of Cheminformatics 2014, 6:21)



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.


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.


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?

Complex Adaptive Dynamical Systems, a Primer

Friday, August 9th, 2013

Complex Adaptive Dynamical Systems, a Primer by Claudius Gros. (PDF)

The high level table of contents should capture your interest:

  1. Graph Theory and Small-World Networks
  2. Chaos, Bifurcations and Diffusion
  3. Complexity and Information Theory
  4. Random Boolean Networks
  5. Cellular Automata and Self-Organized Criticality
  6. Darwinian Evolution, Hypercycles and Game Theory
  7. Synchronization Phenomena
  8. Elements of Cognitive Systems Theory

If not, you can always try the video lectures by the author.

While big data is a crude approximation of some part of the world as we experience it, it is less coarse than prior representations.

Curious how less coarse representations will need to become in order to exhibit the complex behavior of what they represent?

I first saw this at Complex Adaptive Dynamical Systems, a Primer (Claudius Gros) by Charles Iliya Krempeaux.

Expression cartography of human tissues using self organizing maps

Saturday, November 5th, 2011

Expression cartography of human tissues using self organizing maps by Henry Wirth; Markus Löffler; Martin von Bergen; Hans Binder. (BMC Bioinformatics. 2011;12:306)


Parallel high-throughput microarray and sequencing experiments produce vast quantities of multidimensional data which must be arranged and analyzed in a concerted way. One approach to addressing this challenge is the machine learning technique known as self organizing maps (SOMs). SOMs enable a parallel sample- and gene-centered view of genomic data combined with strong visualization and second-level analysis capabilities. The paper aims at bridging the gap between the potency of SOM-machine learning to reduce dimension of high-dimensional data on one hand and practical applications with special emphasis on gene expression analysis on the other hand.

A nice introduction to self organizing maps (SOMs) in a bioinformatics context. Think of them as being yet another way to discover subjects about which people want to make statements and to attach data and analysis.

InTech – Open Access Publisher

Tuesday, March 1st, 2011

I scan lightly before I clean out my spam filter for the blog and saw:

Hello. Yesterday I found two new books about Data mining. These series of books entitled by ‘Data Mining’ address the need by presenting in-depth description of novel mining algorithms and many useful applications. In addition to understanding each section deeply, the two books present useful hints and strategies to solving problems in the following chapters.The progress of data mining technology and large public popularity establish a need for a comprehensive text on the subject. Books are: “New Fundamental Technologies in Data Mining” here & “Knowledge-Oriented Applications in Data Mining” here These are open access books so you can download it for free or just read on online reading platform like I do. Cheers!

I was curious enough to follow the links and was glad I did.

InTech – Open Access Publisher has a number of volumes for downloading that may interest topic mappers. For free!

At first I thought these were article collections, made up of conference and other papers. I have only spot checked Self Organizing Maps – Applications and Novel Algorithm Design, edited by Josphat Igadwa Mwasiagi, but none of the paper titles appear in web searches, other than at

Apologies for appearing suspicious but there is so much re-cycled content on the WWW these days. That does not appear to be the case here, which is welcome news!

Would appreciate hearing of the experience of others with volumes from this site.

Emergent Semantics

Thursday, December 16th, 2010

Philippe Cudré-Mauroux Video, Slides from SOKS: Self-Organising Knowledge Systems, Amsterdam, 29 April 2010


Emergent semantics refers to a set of principles and techniques analyzing the evolution of decentralized semantic structures in large scale distributed information systems. Emergent semantics approaches model the semantics of a distributed system as an ensemble of relationships between syntactic structures.

They consider both the representation of semantics and the discovery of the proper interpretation of symbols as the result of a self-organizing process performed by distributed agents exchanging symbols and having utilities dependent on the proper interpretation of the symbols. This is a complex systems perspective on the problem of dealing with semantics.

A “must see” presentation!

More comments/questions to follow.

Apologies but content/postings will be slow starting today, for a few days. Diagnostic on left hand has me doing hunt-and-peck with my right.