Category Archives: Networks

Kernel Methods are a Hot Topic in Network Feature Analysis

The kernel trick is a well known method in machine learning for producing a real-valued measure of similarity between data points in any number of settings. Kernel methods for network analysis provide a way of assigning real values to vertices of the graph. These values may correspond to similarity across any number of graphical properties such as the neighbours they share, or more dynamic context, the influence that change in the state of one vertex might have on another.

By using the kernel trick it is possible to approximate the distribution of features on the vertices of a graph in a way that respects the graphical relationships between vertices. Kernel based methods have long been used, for instance in inferring protein function from other proteins within Protein Interaction Networks (PINs).

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Graph-based Methods for Cheminformatics

In cheminformatics, there are many possible ways to encode chemical data represented by small molecules and proteins, such as SMILES, fingerprints, chemical descriptors etc. Recently, utilising graph-based methods for machine learning have become more prominent. In this post, we will explore why representing molecules as graphs is a natural and suitable encoding. Continue reading →

Neuronal Complexity: A Little Goes a Long Way…To Clean My Apartment

The classical model of signal integration in both biological and Artificial Neural Networks looks something like this,

f(\mathbf{s})=g\left(\sum\limits_i\alpha_is_i\right)

where g is some linear or non-linear output function whose parameters \alpha_i adapt to feedback from the outside world through changes to protein dense structures near to the point of signal input, namely the Post Synaptic Density (PSD). In this simple model integration is implied to occur at the soma (cell body) where the input signals s_i are combined and broadcast to other neurons through downstream synapses via the axon. Generally speaking neurons (both artificial and otherwise) exist in multilayer networks composing the inputs of one neuron with the outputs of the others creating cross-linked chains of computation that have been shown to be universal in their ability to approximate any desired input-output behaviour.

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Models of learning and memory have relied heavily on modifications to the PSD to explain modifications in behaviour. Physically these changes result from alterations in the concentration and density of the neurotransmitter receptors and ion channels that occur in abundance at the PSD, but, in actuality these channels occur all along the cell wall of the dendrite on which the PSD is located. Dendrites are something like a multi-branched mass of input connections belonging to each neuron. This begs the question as to whether learning might in fact occur all along the length of each densely branched dendritic tree.

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Protein Interaction Networks

Proteins don’t just work in isolation, they form complex cliques and partnerships while some particularly gregarious proteins take multiple partners. It’s becoming increasingly apparent that in order to better understand a system, it’s insufficient to understand its component parts in isolation, especially if the simplest cog in the works end up being part of system like this.

So we know what an individual protein looks like, but what does it actually do?

On a macroscopic scale, a cell doesn’t care if the glucose it needs comes from lactose, converted by lactase into galactose and glucose, or from starch converted by amalase, or from glycogen, or from amino acids converted by gluconeogenesis. All it cares about is the glucose. If one of these multiple pathways should become unavailable, as long as the output is the same (glucose) the cell can continue to function. At a lower level, by forming networks of cooperating proteins, these increase a system’s robustness to change. The internal workings may be rewired, but many systems don’t care where their raw materials come from, just so long as they get them.

Whilst sequence similarity and homology modelling can explain the structure and function of an individual protein, its role in the greater scheme of things may still be in question. By modelling interaction networks, higher level questions can be asked such as: ‘What does this newly discovered complex do’? – ‘I don’t know, but yeast’s got something that looks quite like it.’ Homology modelling therefore isn’t just for single proteins.

Scoring the similarity of proteins in two species can be done using many non-exclusive metrics including:

  • Sequence Similarity – Is this significantly similar to another protein?
  • Gene Ontology – What does it do?
  • Interaction Partners – What other proteins does this one hang around with?


    • Subsequently clustering these proteins based on their interaction partners, highlights the groups of proteins which form functional units. These are highly connected internally whilst having few edges to adjacent clusters. This can provide insight into previously un-investigated proteins which by virtue of being in a cluster of known purpose, their function can be inferred.