Systems Biology and Computational Intelligence

on this manuscript, we report the proposal of the use of computational intelligence methods as a powerful and valuable source of mathematical tools for modeling gene expression networks. Gene expression networks are modeled via transcription networks. Transcription networks are graphoriented mathematical-computational models that attempt to understand gene expression as simpler and smaller systems named network motifs. Each type of transcription network presents a peculiar set of network motifs. Those models of gene expression are part of a bigger scientific field titled in the current state of the art as Systems Biology; which does not look upon individual genes, but plenty of them simultaneously. On the other hand, neural networks are graph-oriented mathematical models with roots on philosophy, physiology, neuroscience, physics, computer science and other scientific branches and they have been promising as mathematical models for modeling

nonparametric data and systems with hidden laws; they are interesting models for mapping spaces of relative high dimensional existence.

Go on: ResearchGate (Full PDF)

Associative memory

In summary, the Hopfield networks are an intelligent self-content addressable system. It recovers bad input based on previous experience, introduced on the system on the training phase. Hopfield network is based on our capability to recognize objects even under noisy environment.

Evolutionary Strategies: third generation?

The particular feature of mutation in ES is that the step sizes are also included in the chromosomes and they themselves undergo variation and selection; genetically it is represented by promoter. It can be called the second generation of evolutionary computing, we can say that the third generation will be taking into account the fact that not just the gene, and controlling area, called promoters, face mutation and selection, but also the communication between the genes, called gene regulatory networks, also known as transcription networks.

MATLAB Implementations and Applications of the Self-Organizing Map

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Kohonen, T., MATLAB Implementations and Applications of the Self-Organizing Map, Unigrafia Oy, Helsinki, Finland, 2014.

See also: Self Organizing Map (SOM) - nice free book

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Self-organizing maps is a quite famous neural network, it is based on the observation that our brain organizes neurons mainly in a 2D configuration. It is a unsupervised neural network, which means, no professor is required to train the net. As Kohonen on the abovementioned book highlights, 

It produces low-dimensional projection images of high-dimensional data distributions,

On this short article, it is presented some excerpts from the abovementioned book. 

This is not a mathematical treatise but a guide. It is neither any handbook of the syntax of computer programming, but rather a starter, which contains a number of exemplary program codes. You may call it a recipe book. 

Since 1989, many SOM software packages have been published by various parties. Usually some diagnostic and other auxiliary programs have been included with the basic SOM algorithms. Some of these packages are freeware, others are commercial, and many researchers use specific SOM programs developed by themselves for particular applications. One might think that the methodology

would already have been established and standardized, but in practice one has encountered following kinds of problems: mentioned on the book. 

The SOM has mainly been used by experts of mathematical statistics and programming. However, with a little of guidance, even non-specialists are expected to be able to use it correctly. So this is not a textbook, which is trying to define the syntax of the complete SOM Toolbox. The purpose of this discourse is to give the first advice in the correct application of the SOM, using exemplary scripts relating to different application areas.