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Michael R. Smith, Mark Clement, Tony Martinez, and Quinn Snell

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1 Time Series Gene Expression Prediction using Neural Networks with Hidden Layers
Michael R. Smith, Mark Clement, Tony Martinez, and Quinn Snell Brigham Young University Department of Computer Science October 2010

2 Modeling Problem

3 Modeling Problem

4 Previous Modeling Work
DNA microarray technology allows for effective and efficient way to measure gene expression Model the gene regulatory network Boolean networks Bayesian networks (dynamic BN) Electrical circuit analysis Differential equations Neural networks Constraint to be interpretable

5 Common NN Implementation
Each node represents a gene Weights represent the effect of one gene on another Positive (activation) Negative (inhibition) Zero (no influence) Perceptron model

6 NN Model Changes Training recurrent neural network is difficult
Backpropagation through time Genetic algorithms Modified the node's function Fuzzy logic Still a perceptron model

7 Challenges with Modeling a GRN
Fundamental Issues Data scarce, noisy and high dimensional No definitive truth Models are constrained to be interpretable Perceptron Issues Chosen because it is interpretable Does not take into higher order correlations Exclusive OR (XOR) problem

8 Revised Problem-Prediction

9 Significance of Prediction
Determine the goodness of the model With a “good” model Use the model to infer the genetic regulatory network Generate additional data points for use in a simpler model Do experiments in silico rather then in vitro.

10 Solution Data scarcity
Create more data by combining data points Examine using multi-layer perceptron (MLPs—NN with hidden layers) for predicting gene expression levels. MLPs are capable of modeling higher order correlations

11 Data Combination

12 Neural Network Models Perceptron— NN without hidden layer
Multi-Layer Perceptron— NN with a hidden layer Recurrent Neural Network

13 DREAM Results

14 DREAM Results

15 DREAM Results

16 SOS Results

17 SOS Results

18 Conclusions MLPs (NNs with hidden layers) are better able to model GRNs than NNs without hidden layers Shows that higher order correlations DO exist in modeling GRNs Could be beneficial in generating synthetic data Data combination for training produces smoother gene expression predictions Noise filtering Similar to Elman nets and BPTT


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