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Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 1 Time Series Gene Expression Prediction using Neural.

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Presentation on theme: "Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 1 Time Series Gene Expression Prediction using Neural."— Presentation transcript:

1 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 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 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 2 Modeling Problem

3 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 3 Modeling Problem

4 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 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 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 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 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 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 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 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 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 8 Revised Problem-Prediction

9 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 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 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 10 Solution Data scarcity Create more data by combining data points Examine using multi-layer perceptron (MLPsNN with hidden layers) for predicting gene expression levels. MLPs are capable of modeling higher order correlations

11 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 11 Data Combination

12 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 12 Neural Network Models Perceptron NN without hidden layer Multi-Layer Perceptron NN with a hidden layer Recurrent Neural Network

13 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 13 DREAM Results

14 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 14 DREAM Results

15 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 15 DREAM Results

16 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 16 SOS Results

17 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 17 SOS Results

18 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 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 Time Series Gene Expression Prediction using NN with Hidden Layers Department of Computer Science 19 QUESTIONS?


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