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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. - Making molecular kernel-based PLM with high confidence. Tandem selection - programmable, no need of index

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slide1

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

slide2

Strategy in This Study

- Making molecular kernel-based PLM with high confidence

  • Tandem selection
  • - programmable, no need of index
  • 2. Enhancing the specificity and confidence using “zinc-finger protein”
slide3

Zinc-Finger Protein

  • DNA binging protein
  • ~30 amino acid
  • used transcriotional regulator domain in cell
  • Codon specific (5’-NNN-3’)
  • Able to expand to recognize 6 or 9 base pair if connected tandemly.
  • - number of attribute increases in 64n
slide4

Magnetic Bead

형광

Attribute

Biotin

형광

T*6

Attribute

classification

Library Data and Attribute Data DNA Design

Library DNA

learning data DNA

DNA library with various DNA length

slide5

Attribute 1의 값에 특이적인 zinc-finger 단백질

Attribute 2의 값에 특이적인 zinc-finger 단백질

Magnetic Bead

Magnetic Bead

형광

형광

Attribute 2

Attribute 1

자석을

이용해

Attribute 1 DNA 회수

자석을

이용해

Attribute 2 DNA 회수

....

Machine Learning with DNA (1)

slide6

Attribute n의 값에 특이적인 zinc-finger 단백질

Class 의 값에 특이적인 zinc-finger 단백질

Magnetic Bead

Magnetic Bead

형광

형광

Attribute n

Class

자석을

이용해

Attribute n DNA 회수

자석을

이용해

Class DNA 회수

Machine Learning with DNA (2)

slide7

T*6

classification

Biotin

형광

Attribute

Extension

Class codon

Extension

TTTTTT

Data Amplification by PCR

slide8

Attribute 1의 값에 특이적인 zinc-finger 단백질

Attribute 2의 값에 특이적인 zinc-finger 단백질

Magnetic Bead

Magnetic Bead

형광

형광

Attribute 2

Attribute 1

자석을

이용해

Attribute 1 DNA 회수

자석을

이용해

Attribute 2 DNA 회수

....

Classification Prediction by Kernel-Based PLM

library

streptavidin으로 library DNA 회수

slide9

Attribute n의 값에 특이적인 zinc-finger 단백질

Class 의 값에 특이적인 zinc-finger 단백질

Magnetic Bead

Magnetic Bead

형광

형광

Attribute n

Class

자석을

이용해

Attribute n DNA 회수

형광

Classification Prediction by Kernel-Based PLM

library

streptavidin으로 library DNA 회수

library design
Library Design

attribute1

attribute2

attribute3

class value

Positive

AAA

AAG

ACA

TTA

Negative

AAC

AAT

ACC

TTC

(a) encoding for zinc-finger Protein

Positive

Positive

Negative

AAA

AAA AAT

AAC ACT ACA

AAA TTA

AAA AAT TTA

AAC ACT TTA

AAA TTC

AAA AAT TTC

AAC ACT TTC

(c) New Library Design

(b) Previous Library Design

learning algorithm
Learning Algorithm

new example e

e is positive ?

yes

no

Why Separation ?

[Tradeoff Negative Pruning]

Why 2 attribute ?

Find SuperSet that

differ in 2 attributes

Find SuperSet that

differ in 2 attributes

[noise of example]

Positive

Negative

(a) Learning Algorithm

classification of new data
Classification of New Data

new data

Positive

Negative

ratio = size of positive Library

/ size of negative Library

a = # of positive data

b = # of negative data

no

a > b * ratio

yes

positive value

negative value

(a) Classification Algorithm

experimental result
Experimental Result

(a) Variation of Library size

experimental result1
Experimental Result

1

2

3

4

Avg

Corrent(120)

112

112

112

112

112

(a) Correctness of 120 example data

1

2

3

4

Avg

Corrent(60)

59

59

59

59

59

(b) Correctness of 60 example data

1

2

3

4

Avg

Corrent(120)

118

118

118

118

118

(a) Correctness of 60 example data

conclusion
Conclusion
  • Zinc-finger Protein
  • No indexing
  • Reasonable Classification
  • 2 Sub Library