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研究生:李雅惠
研究生(外文):Ya-hui Li
論文名稱:運用基因演算法達成生物晶片之最佳品管控制
論文名稱(外文):Optimal Quality Control for Oligo-arrays Using Genetic Algorithm
指導教授:李宗南李宗南引用關係
指導教授(外文):Chung-nan Lee
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊工程學系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2004
畢業學年度:92
語文別:英文
論文頁數:34
中文關鍵詞:基因演算法生物晶片品管控制
外文關鍵詞:oligo arrayquality controlgenetic algorithm
相關次數:
  • 被引用被引用:1
  • 點閱點閱:143
  • 評分評分:
  • 下載下載:18
  • 收藏至我的研究室書目清單書目收藏:0
Oligo array是一種將基因表現量化及大量平行化測量的高產量技術,目前廣泛地運用在生物與醫學方面的研究。在其製造過程之中,若合成的程序執行時產生一個錯誤的步驟,將會影響到所有使用該錯誤步驟的探針。在此論文中,我們採用了兩階段基因演算法來達成oligo array的最佳品管控制,藉此來偵測任何一個單一錯誤的步驟。第一個階段執行廣泛的搜尋以獲得近似解,第二個階段對這些近似解的區域進行細微的搜尋以獲得最佳解。而且,我們所提出的演算法可利用多個個體同時搜尋多個空間。兩階段基因演算法優越的搜尋能力幫助我們找到hill-climbing演算法可以找到的275例子。除此之外,也找到了5種利用hill-climbing演算法無法解決的例子。
Oligo array is a high throughput technology and is widely used in many scopes of biology and medical researches for quantitative and highly parallel measurements of gene expression. When one faulty step occurs during the synthesis process, it affects all probes using the faulty step. In this thesis, a two-phase genetic algorithm (GA) is proposed to design optimal quality control of oligo array for detecting any single faulty step. The first phase performs the wide search to obtain the approximate solutions and the second phase performs the local search on the approximate solutions to achieve the optimal solution. Besides, the proposed algorithm could hold many non-duplicate individuals and parallelly search multiple regions simultaneously. The superior searching capability of the two-phase GA helps us to find out the 275 nonequireplicate cases that settled by the hill-climbing algorithm. Furthermore, the proposed algorithm also discovers five more open issues.
Chapter 1. INTRODUCTION 1
Chapter 2. BACKGROUND MATERIALS 4
2.1 OLIGO ARRAYS 4
2.2 BALANCED BINARY CODES 6
2.3 GENETIC ALGORITHM 8
2.4 LITERATURE REVIEWS 9
Chapter 3. THE PROPOSED ALGORITHM 15
3.1 ENCODING AND DECODING 17
3.2 INITIAL POPULATION 18
3.3 EVALUATION 18
3.4 GA OPERATORS 21
3.4.1 Selection 21
3.4.2 Crossover 21
3.4.3 Mutation 22
Chapter 4. IMPLEMENTATION RESULTS 25
Chapter 5. DISCUSSION 28
Chapter 6. CONCLUSIONS 32
REFERENCES 33
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[4] E. Hubbell and P. A. Pevzner (1999), Fidelity probes for DNA arrays, in Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, AAAI Press, Heidelberg, Germany, pp. 113-117.
[5] R. Sengupta and M. Tompa (2002), Quality control in manufacturing oligo arrays: A combinatorial design approach, Journal of Computational Biology, Vol. 9, pp. 1-22.
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[7] C. J. Colbourn, A. C. H. Ling and M. Tompa (2002), Construction of optimal quality control for oligo arrays, Bioinformatics, Vol. 18, pp. 529-535.
[8] D. E. Goldberg (1989), Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley , New York .
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[12] T. M. Mitchell (1997), Machine Learning. McGraw-Hill, New York, USA. pp. 249-273.
[13] Affymetrix Inc. , Array manufacturing , http://www.affymetrix.com/technology/manufacturing/index.affx
[14] S. Singh-Gasson, R. D. Green, Y. Yue, C. Nelson, F.Blattner, M. R. Sussman and F. Cerrina, (1999) Maskless fabrication of lightdirected oligonucleotide microarrays using a digital micromirror array. Nat. Biotechnol., Vol. 17, pp. 974-978.
[15] J. H. Holland, (1975) Adaptation in Natural and Artificial Systems. University of Michigan Press.
[16] J. H. Chen, S. Y. Le and J.V. Maizel (2000), Prediction of common secondary structures of RNAs:a genetic algorithm approach. Nucleic Acids Research, Vol.28, No.4, pp.991-999.
[17] J. S. Wu, C. N. Lee, C. C. Wu and Y. L. Shiue (2003), Primer Design Using Genetic Algorithm, Bioinformatics, Advance Access published on February 26, 2004.
[18] D. P. Shaver, (1973) Construction of (v , k ,λ)-designs using a non-enumerative search technique, PhD Thesis, Syracuse University.
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