臺灣博碩士論文加值系統

從基因表現資料找出有意義的基因群組,長久以來都是分析微矩陣資料的一個重要課題。由於傳統演算法在先天上的限制,許多雙分群演算法被發展出來,用以解決此問題,並有著不同的目標和策略。我們基於分析頻繁項目集的架構下,在此提出一個雙分群法。和以往較為不同的是,我們把微矩陣資料的雙分群問題,轉換為挖掘頻繁項目集的問題。為了驗證我們演算法可行,我們首先和代表性的數個傳統分群法進行比較 ,而實驗結果顯示我們的方法穩定度和精確度比傳統方法好。接著,我們也和近年來的幾個雙分群系統 ,在已知且公開的資料下,進行一連串比較。最後,將顯示我們演算法在多個測試項目下,確實超越近年來的知名雙分群法。

Finding meaningful clusters of gene expression data has always been one of the most important topics of microarray data analysis. Due to the limitations of conventional clustering algorithms, numerous biclustering methods, with different aims and strategies, have been developed to mitigate the problems. We propose a new biclustering algorithm under the framework of market basket analysis focused on frequent itemset analysis. Unlike previous works, we transform the biclustering problem into a frequent itemset finding task where significant biclusters are described as frequent itemsets. To verify its feasibility, we first compared it with several representative conventional clustering algorithms. The experiments show very promising results. We also conducted a comparative study of current biclustering systems based on the widely-used prior knowledge, Gene Ontology. The study demonstrates that our method significantly outperforms the current biclustering algorithms in our tests.

摘要 7
Abstract 8
致謝 9
目錄 10
第一章--序論 12
1.1 問題源起 12
1.2 論文架構 12
第二章--文獻探討 13
2.1中心法則 13
2.2 調控網路 13
2.2 調控網路 14
2.3雙分群 15
2.4 FP-Tree 演算法 18
第三章--研究方法 20
3.1實驗假設 20
3.2實驗測試資料 22
3.2.1資料格式 23
3.3 實驗前處理 25
3.3.1正規化 25
3.3.2正規化測試分佈圖 26
3.3.3正規化結論 29
3.3.4離散化 30
3.3.5離散化結論 31
3.4 PIFP (Progress Iterative Frequent Pattern-tree)演算法 32
3.4.1 PIFP系統流程圖 35
3.4.2 PIFP輸出結果 37
3.4.3 PIFP特色之處 39
第四章--結果與討論 40
4.1評分準則 40
4.1.1 評分公式:超幾何分佈 41
4.1.2 評分公式:FuncAssociate(The Gene Set Functionator) 42
4.2測試程式 –Clustering Programs 44
4.3測試結果 - Clustering Programs 46
4.3.1 BFM分佈圖 46
4.3.2 基因個數分佈圖 51
4.4結果討論 - Clustering Programs 55
4.5測試程式 –Biclustering Programs 56
4.5.1雙分群參數設定 57
4.6測試結果 - Biclustering Programs 58
4.7結果討論 - Biclustering Programs 63
4.8 FP vs PIFP 64
第五章--結論與展望 65
5.1 結論 65
5.2 未來展望 66
參考文獻 (依照字母順序排列) 67

徐英哲(2003)以基因表現相關性及轉錄因子結合區重建調控網路

Alter O, Brown PO, Botstein D (2000). Singular value decomposition for genome-wide expression data processing and modeling.Proc Natl Acad Sci.;97(18):10101-6.

Becquet C, Blachon S, Jeudy B, Boulicaut JF, Gandrillon O. (2002) Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data. Genome Biol.;3(12)

Ben-Dor A, Chor B, Karp R, Yakhini Z. (2003)Discovering local structure in gene expression data: the order-preserving submatrix problem. J Comput Biol. 10(3-4):373-84.

Bergmann S, Ihmels J, Barkai N (2003). Iterative signature algorithm for the analysis of large-scale gene expression data. Phys Rev E Stat Nonlin Soft Matter Phys. 2003 Mar;67(3 Pt 1)

Berriz GF, King OD, Bryant B, Sander C, Roth FP.(2003)Characterizing gene sets with FuncAssociate.Bioinformatics. 2003 Dec 12;19(18):2502-4.

Cheng Y, Church GM. (2000)Biclustering of expression data. Proc Int Conf Intell Syst Mol Biol. 2000;8:93-103.

Creighton C, Hanash S (2003).Mining gene expression databases for association rules. Bioinformatics. Jan;19(1):79-86.

Dembele D, Kastner P. (2003) ,Fuzzy C-means method for clustering microarray data. Bioinformatics. ;19(8):973-80.

Getz G, Levine E, Domany E. (2000) Coupled two-way clustering analysis of gene microarray data.Proc Natl Acad Sci ;97(22):

Hashimoto RF, Kim S, Shmulevich I, Zhang W, Bittner ML, Dougherty ER.(2004) Growing genetic regulatory networks from seed genes. Bioinformatics ;20(8):1241-7.

Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stepaniants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M, Friend SH.(2000) Functional discovery via a compendium of expression profiles .Cell. Jul 7;102(1):109-26.

Ihmels JH, Bergmann S. (2004), Challenges and prospects in the analysis of large-scale gene expression data.Brief Bioinform.;5(4):313-27.

Ihmels J, Friedlander G, Bergmann S, Sarig O, Ziv Y, Barkai N (2002). Revealing modular organization in the yeast transcriptional network. Nature Genetic;31(4):370-7.

Ihmels J, Bergmann S, Barkai N.(2004) Defining transcription modules using large-scale gene expression data. Bioinformatics;20(13):1993-2003.

J.Han, J.Pei ,and Y.Yin (2000) .Mining frequent patterns without candidate generation.SIGMOD’00 ,pages 1-12 ,

Ji L, Tan KL (2004). Mining gene expression data for positive and negative co-regulated gene clusters. Bioinformatics.;20(16):2711-2718.

Kloster M, Tang C, Wingreen NS.(2005) Finding regulatory modules through large-scale gene-expression data analysis. Bioinformatics.(7) 1172-1179.

Michael B. Eisen, Paul T. Spellman, Patrick O. Brown, and David Botstein,(1998) Cluster analysis and display of genome-wide expression patterns ,PNAS ,Vol. 95, Issue 25, 14863-14868,

Murali TM, Kasif S.(2003) Extracting conserved gene expression motifs from gene expression data. Pac Symp Biocomput. 2003;77-88.

Prelic A, Bleuler S, Zimmermann P, Wille A, Buhlmann P, Gruissem W, Hennig L, Thiele L, Zitzler E.A (2006) systematic comparison and evaluation of biclustering methods for gene expression data.Bioinformatics. 2006 May 1;22(9):1122-1129.

Qian J, Dolled-Filhart M, Lin J, Yu H, Gerstein M. (2001) Beyond synexpression relationships: local clustering of time-shifted and inverted gene expression profiles identifies new, biologically relevant interactions.J Mol Biol ;314(5):1053-1066.

Qiu X, Brooks AI, Klebanov L, Yakovlev N.(2005) The effects of normalization on the correlation structure of microarray data. BMC Bioinformatics.;6(1):120.

Rice JJ, Tu Y, Stolovitzky G.(2005) Reconstructing biological networks using conditional correlation analysis.Bioinformatics.;21(6):765-773.

Sara C. Madeira ,Arlindo L. Oliveira,(2004) Biclustering Algorithms for Biological Data Analysis: A Survey , IEEE / TCBB Volume 1 , Issue 1 ) Pages: 24 - 45

Sheng Q, Moreau Y, De Moor B. (2003) Biclustering microarray data by Gibbs sampling. Bioinformatics. ;19 Suppl 2:II196-II205.

Shmulevich I, Dougherty ER, Kim S, Zhang W. (2002) Probabilistic Boolean Networks: a rule-based uncertainty model for gene regulatory networks Bioinformatics;18(2) : 261-74.

Tanay A, Sharan R, Shamir R.(2002) Discovering statistically significant biclusters in gene expression data.Bioinformatics.;18 Suppl 1:S136-44.

Torrente A, Kapushesky M, Brazma A. (2005) A new algorithm for comparing and visualizing relationships between hierarchical and flat gene expression data clusterings. Bioinformatics.;21(21):3993-3999.