Analyzing Promoter Sequences with Multilayer Perceptrons.ppt

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1、Analyzing Promoter Sequences with Multilayer Perceptrons,Glenn Walker ECE 539,Background (DNA),Deoxyribonucleic acid (DNA) is a long molecule made up of combinations of four smaller molecules (base pairs): adenine (A), cytosine (C), guanine (G), thymine (T). These four molecules are combined in an o

2、rder unique to each living organism. The order of the molecules contains the information to make all the parts necessary for any organism to survive.,DNA is two-stranded and complementary,Background (DNA),Genes are sections of DNA that can contain from a few hundred base-pairs to tens of thousands.

3、Genes contain instructions on how to make proteins - molecules necessary for building and maintaining organisms.,Three different genes on piece of DNA,“junk” DNA,Background,Promoters are sequences of DNA to which RNA polymerase can bind and begin transcription of a gene. Transcription is the process

4、 of making a complementary copy of the DNA which is then translated into a protein.,promoter sequence,actual gene information,RNA polymerase binds here and begins transcription,Problem,Knowing gene locations is desirable for medical reasons One way to find genes is to look for promoter regions How d

5、o we find promoter regions?,One Solution,Promoter regions are highly conserved - different regions often contain similar patterns We can train neural networks to recognize promoter regions We choose a multilayer perceptron,Neural Network Configuration,The multilayer perceptron (MLP) is a very common

6、 neuralnetwork configuration We used a MLP with 3 layers - an input, output, and hiddenlayer,Number of:,Inputs,Hidden,Output,1,115/58,4,8,16, 20,24,28, 32,Neural Network Configuration,Two ways of presenting input were tried - one used 58 inputs and the other 115 Different numbers of hidden nodes wer

7、e tried to find the optimally structured neural network Only one output was used to indicate whether the input was a promoter sequence or not (1 or 0, respectively),Neural Network Inputs,The inputs consisted of 106 sets of 57 bases of DNA. 53 were promoters and 53 were not. One of the input promoter

8、sequences:,TACTAGCAATACGCTTGCGTTCGGTGGTTAAGTATGTATAATGCGCGGGCTTGTCGT,The input was presented to the neural network in two ways:,A 00 C 01 G 10 T 11,A 0.2 C 0.4 G 0.6 T 0.8,114 input neurons,57 input neurons,Neural Network Training,Each configuration was run 10 times. Within each of the 10 runs, 106

9、runs were performed. For each of these, 105 of the promoter sequences were used for training with the 106th used for testing. The testing sequences were changed for each of the 106 runs so that each sequence was the test sequence only once.Ten runs were necessary since weights for the MLP were initi

10、alized to random values which might have led to different classifications for the same input sequence.,Hidden Nodes vs. Classification Rate,Scaled Input vs. Classification Rate,Compared to Others,Walker (NN) 78% ONeil (NN) 83% Towell (KBANN) 90% ONeil (Rule-based) 70% ID3 (Decision tree) 76%,Conclus

11、ion,Not the best but not the worst Using a hybrid technique would improve results The MLP is a very useful tool for the field of bioinformatics,References,Harley, C. B. and Reynolds, R. P. 1987. Analysis of E. coli promoter sequences. Nucleic Acids Research, 15(5):2343-2361. ONeill, M. C. 1991. Trai

12、ning back-propagation neural networks to define and detect DNA-binding sites. Nucleic Acids Research, 19(2):313-318. Quinlan, J. 1986. Induction of decision trees. Machine Learning, 1:81-106. Towell, G. G., Shavlik, J. W., and Noordewier, M. O. 1990. Refinement of Approximate Domain Theories by Knowledge-Based Neural Networks. AAAI-90, 861-866.,