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

19. QUESTIONS?