Saturday, July 26, 2014

Introduction

Systems Biology and learning machine share a commonplace on their endeavors. My main motivation for creating this blog was the decision of Wikipedia to delete my article Systems Biology and Machine Learning. Further, I have spent some time on this direction  for a while. 




As highlights Hamid (2008)

"If we could start with complete knowledge of a system, we might construct a very detailed model and then find approximation that hold true under conditions of interest. However, in GRN modeling, we are often faced with the opposite situation. We do not know the mechanisms that cause an observed outcome. In such cases, we typically construct models in three steps: 1) use biological knowledge to hypothesize the nature of the interactions in the system; 2) propose an approximate model (e.g a rate law) for each interaction; and 3) find parameters values that minimize the error between the model and the data. " This and other statements surely pinpoint the natural sinergy between systems biology and machine learning based models.

Reference

PIRES J. G. On the applicability of Computational Intelligence in Transcription Network Modeling. Thesis of master of science. Faculty of Applied Physics and Mathematics, Gdansk University of Technology, Poland. 74:1:46. 2012.

Pires JG (2013). On the mathematical modelling in gene expression estimation. II Workshop and School on Dynamics, Transport and Control in Complex Networks  (ComplexNet), Ribeirão Preto, SP, Brazil. 21-26/October. Poster.

Pires JG (2013). Neural Networks in Transcription Networks: An alternative and complementary approach for the observer-based method. 1st BRICS Countries  & 11th Brazilian Congress on Computational Intelligence. Brazil. 2: 1-2.

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Systems Biology and Machine Learning

Systems Biology and Machine Learning