gene expression classification by kernel-based plm 응용화학부 2004-31012 서 주 현...
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Gene Expression Classification
by Kernel-based PLM
응용화학부 2004-31012 서 주 현
전기전자공학부 2003-21710 조 율 원
컴퓨터공학과 2004-21440 강 성 구
Strategy in This Study
1. Tandem selection
- programmable, no need of index
2. Enhancing the specificity and confidence using “zinc-finger protein”
- Making molecular kernel-based PLM with high confidence
Zinc-Finger Protein 1. DNA binging protein
2. ~30 amino acid
3. used transcriotional regulator domain in cell
4. Codon specific (5’-NNN-3’)
5. Able to expand to recognize 6 or 9 base pair if connected tandemly.
- number of attribute increases in 64n
형광Magnetic BeadAttribut
e
Biotin 형광 T*6 Attribute classification
Library Data and Attribute Data DNA Design
Library DNA
learning data DNA DNA library with various DNA length
형광Magnetic Bead Attribute 1 형광 Attribute 2
Magnetic Bead
자석을
이용해
Attribute 1 DNA 회수
Attribute 1 의 값에 특이적인 zinc-finger 단백질
Attribute 2 의 값에 특이적인 zinc-finger 단백질
자석을
이용해
Attribute 2 DNA 회수
....
Machine Learning with DNA (1)
형광 Attribute n
Magnetic Bead
Attribute n의 값에 특이적인 zinc-finger 단백질
자석을
이용해
Attribute n DNA 회수
형광 ClassMagnetic Bead
Class 의 값에 특이적인 zinc-finger 단백질
자석을
이용해
Class DNA 회수
Machine Learning with DNA (2)
Biotin 형광 T*6 Attribute
classification
Class codonExtension
TTTTTTExtension
Data Amplification by PCR
Classification Prediction by Kernel-Based PLM
형광Magnetic Bead Attribute 1 형광 Attribute 2
Magnetic Bead
자석을
이용해
Attribute 1 DNA 회수
Attribute 1 의 값에 특이적인 zinc-finger 단백질
Attribute 2 의 값에 특이적인 zinc-finger 단백질
자석을
이용해
Attribute 2 DNA 회수
....
streptavidin 으로 library DNA 회수library
형광 Attribute n
Magnetic Bead
Attribute n의 값에 특이적인 zinc-finger 단백질
자석을
이용해
Attribute n DNA 회수
형광 ClassMagnetic Bead
Class 의 값에 특이적인 zinc-finger 단백질
형광
Classification Prediction by Kernel-Based PLM
librarystreptavidin 으로 library DNA 회수
Library Design
(b) Previous Library Design (c) New Library Design
Positive
Negative
attribute1
AAA
AAC
attribute2
AAG
AAT
attribute3
ACA
ACC
…
…
…
class value
TTA
TTC
(a) encoding for zinc-finger Protein
Positive Positive Negative
AAA AAA AATAAC ACT ACA
AAA TTA AAA AAT TTAAAC ACT TTA
AAA TTC AAA AAT TTCAAC ACT TTC
Learning Algorithmnew example e
e is positive ?
Positive Negative
yes no
Find SuperSet thatdiffer in 2 attributes
Find SuperSet thatdiffer in 2 attributes
(a) Learning Algorithm
Why Separation ?
Why 2 attribute ?
[Tradeoff Negative Pruning]
[noise of example]
Classification of New Datanew data
Positive Negative
(a) Classification Algorithm
a = # of positive datab = # of negative data
a > b * ratio
positive value negative value
yes
no
ratio = size of positive Library/ size of negative Library
Experimental Result
0
10000
20000
30000
40000
50000
60000
70000
1 2 3 4 5 6 7 8 9 10 11 12 13
# of example
file
siz
e (
Byte
)
1계열2계열
(a) Variation of Library size
Experimental Result
Corrent(120)
1
112
2
112
3
112
4
112
Avg
112
(a) Correctness of 120 example data
Corrent(60)
1
59
2
59
3
59
4
59
Avg
59
(b) Correctness of 60 example data
Corrent(120)
1
118
2
118
3
118
4
118
Avg
118
(a) Correctness of 60 example data
Conclusion
• Zinc-finger Protein• No indexing• Reasonable Classification • 2 Sub Library
