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.
This blog is about systems Biology and Machine Learning, mainly my works on the area so far
