1. S.N.U. EECS Jeong-Jin Lee Eui-Taik Na
Locating exons
S.N.U. EECS
Jeong-Jin Lee
Eui-Taik Na

2. Using Multilayer Neural Network Approach
Part I.
Using Multilayer Neural Network Approach

3. System Architecture Output Input Representation Learning
Finding exon starting
region and ending region
Input Representation
Orthogonal encoding
Using windows
[0, 0, 0, 0] ~ outside the sequences
Learning
Backpropagation

4. Parameters 1 Interpretations of inputs Case 1 - aligning the center
- aligning the end
Case 3, 4
- biased

5. Parameters 2
The size of window
Number of units in the hidden layer

6. Experimental Results 1 Three datasets from UCSC Data set 1 Data set 2
Having 7 subsets
Data set 1
3447 trainings, 251 evaluations
Data set 2
3448 trainings, 250 evaluations
Evaluation Score
Sums of success hits’ percentages

7. Experimental Results 2
According to window type

8. Experimental Results 3
According to window size

9. Experimental Results 4
According to number of hidden units

10. Part 2
Using HMMs

11. Using HMMs
The Tied Model
The Wheel Model

12. Future considerations
HMM + NN mixed models