Artificial Neural Network usually called (NN), it is a computational model that is inspired by the biological nervous system. The network is composed by simple elements called artificial neurons that are interconnected and operate in parallel. In most cases the NN is an adaptive system that can change its structure depending on the internal or external information that flow into the network during the learning process. The NN can be trained to perform a particular function by adjusting the values of the connection (weights) between the artificial neurons.
During the learning process the difference between the desired output (target) and the network output is minimized. This difference is usually called cost; the cost function is the measure of how far is the network output from the desired value. A common cost function is the mean-squared error and there are several algorithms that can be used
to minimize this function.
Figure 1: Normally Neural Networks are trained so that a particular input leads to a specific target output. Neural networks have been trained to perform complex functions in various fields, including pattern recognition, identification, classification, speech, vision, and control systems.
Model description
The NN model has been created with the MATLAB NN-toolbox. The input/target pairs used to train the network comprise experimental and literature data (Bartel et al. 2007). The experimental data were obtained by measuring via luciferase assay the strength of knockdown due to the interaction between the shRNA and the binding site situated on the 3’UTR of luciferase gene. Nearly 30 different rational designed binding sites were tested and the respective knockdown strength calculated with the following formula->(formula anyone???).
Input/target pairs
Each input was represented by a four elements vector. Each element corresponded to a score value related to a specific feature of the binding site. The features used to describe the binding site were: seed type, the 3’pairing contribution the AU-content and the number of binding site. Each input was paired to the percentage of knockdown related to that particular binding site (target).
Once the network was trained than it was used to predict percentages of knockdown given certain inputs. The predictions were then validated experimentally.
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