Team:Heidelberg/Modeling

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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.  
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.  
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.  
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  [[Image:network.gif]]
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  [[Image:network.gif|400px|center]]
Figure 1: Normally Neural Networks are trained so that a particular input leads to a specific target output.
Figure 1: Normally Neural Networks are trained so that a particular input leads to a specific target output.

Revision as of 00:57, 25 October 2010

Modeling of binding site efficiency

shRNA binding sites

miBSdesigner

Neural Network Model

Neural Network theory

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.

Network.gif

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 four features used to describe the binding site were: seed type, the 3’pairing contribution the AU-content and the number of binding site. The input/target pair represented the relationship between a particular binding site and the related percentage of knockdown. Once the network was trained than it was used to predict percentages of knockdown given certain inputs. The predictions were then validated experimentally.

Characteristic of the Network

Results

Fuzzy Inference Model

shRNA binding sites

3 MFs for height input
MembershipFunction1.png
The height input is...


MF - "big" MF for ON/OFF-system
MembershipFunctionBig.png MembershipONOFF.png
bla bla

Integration on GUI

Tissue specific miRNAs

Integration into GUI

Contents

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