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博碩士論文 etd-0713103-183615 詳細資訊
Title page for etd-0713103-183615
論文名稱
Title
從生物晶片的資料建立與酵母菌 DNA 修補 與 DNA 重組基因相關的基因網路
Constructing gene network of the 51 genes related to Yeast (Saccharomyces cerevisiae) DNA repair and DNA recombination from microarray data
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
71
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2003-06-06
繳交日期
Date of Submission
2003-07-13
關鍵字
Keywords
釀酒酵母菌
saccharomyces cerevisia
統計
Statistics
本論文已被瀏覽 5767 次,被下載 1926
The thesis/dissertation has been browsed 5767 times, has been downloaded 1926 times.
中文摘要
從生物晶片的資料中建立51個與酵母菌 DNA 修補與 DNA 重組基因相關的基因網路並且分析之.在這篇裡,我們只考慮兩種獨立的同步化方法:Alpha factor 及 cdc28.

我們將這51個基因作階層式群集分析及非階層式群集分析(k-means).由這51個基因做分群結果後的圖來看,得知階層式群集分析會比非階層式群集分析(k-means)的分群結果來的好.

基因與基因間可能會有時間上延遲的關係,所以我們需要訂立一個標準來建立這51個基因間的關係,而滿足此標準的基因共有16個.由基因網路的圖中可看出這16個基因彼此活化及抑制的情形.另外,在階層式群集分析後的圖中可看出此16個基因有同樣被分在同一子群內的情形發生.

我們的結果是希望能提供一個有效地節省實驗上之時間的方法.

Abstract
Fifty one yeast genes related to DNA repair and DNA recombination from microarray expression data were analyzed to construct the gene network. In this study, we consider the microarray data sets from two independent synchronized methods: Alpha factor and cdc28.

Hierarchical clustering algorithm and K-means are applied to cluster the 51 genes. The clustering pictures of the 51 genes, we show that Hierarchical clustering out performs the k-means.

Multiple lag correlations are need as the criterion to construct the gene networks of these 51 genes, among which 16 genes are extrapolated. The gene networks of the 16 genes is useful to find out activator or repressor genes. In addition, the 16 genes are clustered into the same subcluster by Hierarchical clustering method.

The results of our studies provide a useful economic and time saving experiment procedure.
目次 Table of Contents
1. Introduction ................................................. 1
2. Biology background and Literature review ..................... 4
2.1 Saccharomyces cerevisiae and The cell-cycle ................. 4
2.2 Spellman's result ........................................... 6
2.3 Aggregate different gene expression data sets and Cubic spline interpolation ................................................... 7
3. Statistic's Methods to construct gene network ............... 11
3.1 Data Manipulation .......................................... 11
3.2 Clustering Analysis ........................................ 12
3.3 Time-series Analysis ....................................... 20
3.4 Make two experiments comparable ............................ 21
3.5 Cross spectrum analysis .................................... 23
4 Results ...................................................... 26
4.1 Clustering Analysis ........................................ 26
4.2 Time-series Analysis ....................................... 31
4.3 Make two experiments comparable ............................ 36
4.4 A linear difference eq. approach ........................... 37
4.5 Cross spectrum analysis of 16 genes ........................ 40
5. Future work ................................................. 42
References .................................................... 43
Figures and Tables and Programs................................ 45
參考文獻 References
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[2] Ziv Bar-joseph, Georg Gerber, David K. Gifford, Tommi S. Jaakkola, Itamar Simon. A new approach to analyzing gene expression time series data. In The Sixth Annual International Conference on Research in Computational Molecular
Biology, 2002.

[3] G.Zhu, Spellman T. S., and et al. Two yeast forkhead genes regulate cell cycle and pseudohyphal growth. Nature, 406:90-94, 2000.

[4] J. Aach and G. M. Church. Aligning gene expression time series with time warping algorithms. Bioinformatics, 17:495-508, 2001.

[5] P. D'haeseleer, X. Wen, S. Fuhrman, and R. Somogyi. Linear modeling of mrna expression levels during cns development and injury. In PSB99, 1999.

[6] O.troyanskaya, M. Cantor, and et al. Missing value estimation methods for dna microarrays. Bioinformatics, 17:520-525, 2001.

[7] B. Brumback and J. Rice Smoothing spline models for the analysis of nested and crossed samples of curves. Am. Statist. Assoc., 93:961-976, 1998.

[8] Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA , Vol 95, pp. 14863-14868, 1999.

[9] Sokal, R. R., Michener, C. D. Univ. Kans. Sci. Bull. 38., 1409-1438, 1958.

[10] Jardine, N., and Sibson, R., Mathematical
Taxonomy, London: Wiley. 1971.

[11] Wishart, D., A generalised approach to cluster analysis. Part of Ph.D. Thesis, University of St. Andrews. 1971.

[12] Strauss, J. S., Bartko, J. J., and Ccarpenter, W. T., "The use of clustering techniques for the classification of
psychiatric patients," British Journal of Psychiatry, 122, 531-540. 1973.
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