1. Gene Structure Prediction Using Neural Networks and Hidden Markov Models
June 18, 2001
권동섭
신수용
조동연

2. Data Sets UCSC data Preprocessing Multiple exon genes
7 Fold Cross validation
Preprocessing
SNNS pattern definition file V3.2
generated at Wed May 16 17:00:
No. of patterns : 16
No. of input units : 48
No. of output units : 4
# Input pattern 1 :
# Output pattern 1:
# Input pattern 2 :
# Output pattern 2:
Multi_exon_GB.dat
pre-propessor

3. Classification Problem
5 Classes
1. Start – Exon
2. Exon – Intron
3. Intron – Exon
4. Exon – End
5. Others
Imbalanced data problem
Boundary : Others = 1 : 9 1 2 3 4

4. Training Data Input Data Output Data Boundary Sequences Others
ATGCGA | GCATGA
Others
GCAGCCAGCTAC or GA | CATGATTTCA
Encoding
A: 0001, C: 0010, G: 0100, T: 1000
Output Data
Boundary: 1 – 0001, 2 – 0010, 3 – 0100, 4 – 1000
Internal: 0000

5. Neural Networks SNNS (version 4.2) Structure
Input: 48
Hidden: 96
Output: 4
Learning: Standard BP with momentum
Learning rate: 0.2
Momentum: 0.1
Maximum difference: 0.1

6. Experimental Setup Training Test Group 0 ~ 5 Online Learning
Boundary: 3068
Others: 27612
Online Learning
Random order
Test
Group 6
2 genes: HUMELAFIN and HSCPH

7. Results – Training Performance
Early Stopping: 260 (0.85%)
SSE

8. Results – Test Performance
HUMELAFIN (6 boundaries)
HSCPH70 (8 boundaries)
Re = 4/6
Pre = 4/48
Re = 5/10
Pre = 5/136

9. Hidden Markov Models Simple Structure Training Test
Construct each HMM for 4 boundary classes
Input: fixed size sequences for each class
Test
Compare generation probabilities
Threshold value