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

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Time Series Gene Expression Prediction using NN with Hidden LayersDepartment of Computer Science

Time Series Gene Expression Prediction using Neural Networks with Hidden LayersMichael R. Smith, Mark Clement, Tony Martinez, and Quinn SnellBrigham Young UniversityDepartment of Computer ScienceOctober 2010

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Modeling Problem0 0 0 0 0 0 0 0 242.81 738.59 0

242.81 738.59 0 1696.68 1252.28 90.61 0 43.6 341.77 1726.66 0341.77 1726.66 0 1942.6 1407.59 24.26 0 53.47 268.56 2161.13 70.48268.56 2161.13 70.48 2103.7 1456.77 0 29.94 57.52 144.02 1921.13 142.1144.02 1921.13 142.1 2006.84 1409.57 0 64.14 71.92 103.53 1619.07 151.37103.53 1619.07 151.37 1847.11 1260.02 0 60.05 60.96 83.96 1190.89 157.9983.96 1190.89 157.99 1653.85 1043.75 64.61 36.96 56.86 92.25 849.25 178.5392.25 849.25 178.53 1431.61 841.59 54.9 16.56 35.04 96.47 655.36 156.8796.47 655.36 156.87 1171.33 571.88 39.93 0 19.44 117.24 494.62 156.18117.24 494.62 156.18 949.78 352.27 24.3 0 19.73 118.91 439.24 142.39118.91 439.24 142.39 793.28 244.06 11.74 0 28.03 91.13 368.75 122.7191.13 368.75 122.71 688.29 231.95 9.12 0 34.8 88.77 382.23 86.7888.77 382.23 86.78 634.7 206.79 23.7 13.66 40.76 90.12 337.57 70.5190.12 337.57 70.51 566.7 207.63 28.91 5.71 51.63 116.57 257.76 47.23116.57 257.76 47.23 494.68 227.32 26.35 3.59 31.75 109.38 292.92 30.78109.38 292.92 30.78 472.41 229.77 15.64 12.8 20.6 98.5 287.02 48.43

98.5 287.02 48.43 464.72 210.75 5.78 0 28.23 73.28 227.66 57.3873.28 227.66 57.38 394.52 185.44 0 0 29.59 50.04 216.05 34.1750.04 216.05 34.17 383.91 165.25 0 2.23 23.58 45.99 238.93 36.3445.99 238.93 36.34 432.23 153.79 0 0.34 35.15 40.07 210.18 29.6940.07 210.18 29.69 395.71 143.59 12.61 2.98 40.93 58.33 183.95 15.0158.33 183.95 15.01 373.94 143.98 27.96 0.46 36.06 58.51 165.03 19.8

uvrD lexA umuDC recA uvrA uvrY ruvA polB sdI icD dudE

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Modeling Problem0 0 0 0 0 0 0 0 242.81 738.59 0

242.81 738.59 0 1696.68 1252.28 90.61 0 43.6 341.77 1726.66 0341.77 1726.66 0 1942.6 1407.59 24.26 0 53.47 268.56 2161.13 70.48268.56 2161.13 70.48 2103.7 1456.77 0 29.94 57.52 144.02 1921.13 142.1144.02 1921.13 142.1 2006.84 1409.57 0 64.14 71.92 103.53 1619.07 151.37103.53 1619.07 151.37 1847.11 1260.02 0 60.05 60.96 83.96 1190.89 157.9983.96 1190.89 157.99 1653.85 1043.75 64.61 36.96 56.86 92.25 849.25 178.5392.25 849.25 178.53 1431.61 841.59 54.9 16.56 35.04 96.47 655.36 156.8796.47 655.36 156.87 1171.33 571.88 39.93 0 19.44 117.24 494.62 156.18117.24 494.62 156.18 949.78 352.27 24.3 0 19.73 118.91 439.24 142.39118.91 439.24 142.39 793.28 244.06 11.74 0 28.03 91.13 368.75 122.7191.13 368.75 122.71 688.29 231.95 9.12 0 34.8 88.77 382.23 86.7888.77 382.23 86.78 634.7 206.79 23.7 13.66 40.76 90.12 337.57 70.5190.12 337.57 70.51 566.7 207.63 28.91 5.71 51.63 116.57 257.76 47.23116.57 257.76 47.23 494.68 227.32 26.35 3.59 31.75 109.38 292.92 30.78109.38 292.92 30.78 472.41 229.77 15.64 12.8 20.6 98.5 287.02 48.43

98.5 287.02 48.43 464.72 210.75 5.78 0 28.23 73.28 227.66 57.3873.28 227.66 57.38 394.52 185.44 0 0 29.59 50.04 216.05 34.1750.04 216.05 34.17 383.91 165.25 0 2.23 23.58 45.99 238.93 36.3445.99 238.93 36.34 432.23 153.79 0 0.34 35.15 40.07 210.18 29.6940.07 210.18 29.69 395.71 143.59 12.61 2.98 40.93 58.33 183.95 15.0158.33 183.95 15.01 373.94 143.98 27.96 0.46 36.06 58.51 165.03 19.8


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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

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Common NN ImplementationEach node

represents a geneWeights represent

the effect of one gene on anotherPositive (activation)Negative (inhibition)Zero (no influence)

Perceptron model

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NN Model Changes Training recurrent neural

network is difficultBackpropagation through time

Genetic algorithms

Modified the node's functionFuzzy logic

Still a perceptron model

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Challenges with Modeling a GRN Fundamental Issues

Data scarce, noisy and high dimensionalNo definitive truthModels are constrained to be interpretable

Perceptron IssuesChosen because it is interpretableDoes not take into higher order correlations

Exclusive OR (XOR) problem

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Revised Problem-Prediction0 0 0 0 0 0 0 0 242.81 738.59 0

242.81 738.59 0 1696.68 1252.28 90.61 0 43.6 341.77 1726.66 0341.77 1726.66 0 1942.6 1407.59 24.26 0 53.47 268.56 2161.13 70.48268.56 2161.13 70.48 2103.7 1456.77 0 29.94 57.52 144.02 1921.13 142.1144.02 1921.13 142.1 2006.84 1409.57 0 64.14 71.92 103.53 1619.07 151.37103.53 1619.07 151.37 1847.11 1260.02 0 60.05 60.96 83.96 1190.89 157.9983.96 1190.89 157.99 1653.85 1043.75 64.61 36.96 56.86 92.25 849.25 178.5392.25 849.25 178.53 1431.61 841.59 54.9 16.56 35.04 96.47 655.36 156.8796.47 655.36 156.87 1171.33 571.88 39.93 0 19.44 117.24 494.62 156.18117.24 494.62 156.18 949.78 352.27 24.3 0 19.73 118.91 439.24 142.39118.91 439.24 142.39 793.28 244.06 11.74 0 28.03 91.13 368.75 122.7191.13 368.75 122.71 688.29 231.95 9.12 0 34.8 88.77 382.23 86.7888.77 382.23 86.78 634.7 206.79 23.7 13.66 40.76 90.12 337.57 70.5190.12 337.57 70.51 566.7 207.63 28.91 5.71 51.63 116.57 257.76 47.23116.57 257.76 47.23 494.68 227.32 26.35 3.59 31.75 109.38 292.92 30.78109.38 292.92 30.78 472.41 229.77 15.64 12.8 20.6 98.5 287.02 48.43

98.5 287.02 48.43 464.72 210.75 5.78 0 28.23 73.28 227.66 57.3873.28 227.66 57.38 394.52 185.44 0 0 29.59 50.04 216.05 34.1750.04 216.05 34.17 383.91 165.25 0 2.23 23.58 45.99 238.93 36.3445.99 238.93 36.34 432.23 153.79 0 0.34 35.15 40.07 210.18 29.6940.07 210.18 29.69 395.71 143.59 12.61 2.98 40.93 58.33 183.95 15.0158.33 183.95 15.01 373.94 143.98 27.96 0.46 36.06 58.51 165.03 19.8


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Significance of Prediction Determine the goodness of the model

With a “good” modelUse the model to infer the genetic regulatory networkGenerate additional data points for use in a simpler modelDo experiments in silico rather then in vitro.

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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

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Data CombinationTime G1 G210 a b20 c d30 e f

G1_in G2_in G1_out G2_outa b c dc d e f

Delta G1_in G2_in G1_out G2_out10 a b c d20 a b e f10 c d e f

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Neural Network Models Perceptron—

NN without hidden layer

Multi-Layer Perceptron—NN with a hidden layer

Recurrent Neural Network

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DREAM Results

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DREAM Results

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DREAM Results

Original Data MLP

MLP with Time BPTT

Perceptron

Elman net

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SOS Results

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SOS Results

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Conclusions MLPs (NNs with hidden layers) are better able to

model GRNs than NNs without hidden layersShows that higher order correlations DO exist in modeling GRNs

Could be beneficial in generating synthetic data

Data combination for training produces smoother gene expression predictionsNoise filteringSimilar to Elman nets and BPTT

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QUESTIONS?

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

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