博碩士論文 945202050 詳細資訊




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姓名 葉韋伯(Wei-bo Ye)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 探討在生物調控網路上的基因表現之間的關係
(Discovering correlation between expressions of genes on biological pathway)
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摘要(中) 結合生物微晶片實驗與電腦計算分析是目前研究癌症的一項新興科技,藉由上萬個基因表現來預測癌症的各項顯示特徵是否出現,甚至找出規則以了解癌症的成因,影響的方式,並且發展藥物療程來抑制癌症。不只癌症,任何未知的疾病都適用此方法。而利用基因表現的差異來判斷疾病的特徵或成因是目前大部分研究的議題,此議題中單就基因表現量不同所選出來的基因也許數量上非常多,並且無法直接觀察這些基因之間的調控關係,導致生物意義上的顯示不足。
於是我們設計一個方法,先觀察在生物調控網路上,有調控關係的基因,再探討這些基因間,基因表現量的關係分別在乳癌復發與不復發之間的差異性,如此提高資料可信度,並可以觀察出傳統基因表現差異選取無法看出的生物意義。
我們的方法可以在結合不同的實驗資料的情況下,保持極高的穩定度,並達成我們的目的,得到真正有鑑別力的基因與其調控關係,大大降低生物技術實驗的成本。
摘要(英) The method of different expressed genes for microarray gene expression data can help find the factor which affect diseases. But differentially expressed genes are selected may be too many and we can’t get their correlation each other. Therefore, we design a method to attempt to avoid the problem and discover more biological meanings than traditional method. We select a gene pair set of interacting genes in biological pathway and discuss correlations of expressions of gene pairs in different condition (relapse and non-relapse in breast cancer) by microarray gene expression data. Furthermore, we test on breast cancer relapse and non-relapse datasets to demonstrate that our method is useful and reliable according to the good result. And we get very stable result in in the same platform.
關鍵字(中) ★ 基因表現
★ 基因調控網路
★ 共同表現
關鍵字(英) ★ gene expression
★ biological pathway
★ co-expression
論文目次 Chapter 1 Introduction .….…………………………………...……...1
1.1 Background …..……………………………………………..………1
1.2 Motivation ……………………………………………………..2
1.3 Goal………………..…………...……………………………………………2
Chapter 2 Related Works ………………………………………………….5
2.1 Tools of analysis of gene expression in biological pathway.….…..5
2.1.1 Pathway Miner ……………………………………………….………5
2.1.2 ArrayXPath ………………………………………………..…………6
2.1.3 Ingenuity pathway analysis (IPA)……………....…………………….8
2.2 KEGG……………………..................…………………………………….9
2.2.1 KEGG API …………………………………………………………..9
Chapter 3 Materials and Data……………………………………………..10
3.1 Breast cancer……..……………………………………………………..10
3.1.1 Non-relapse and relapse of breast cancer……………………...…….10
3.2 Breast cancer no-relapse and relapse in GEO………………..……..11
Chapter 4 Method ………………………….………………………………..12
4.1 System flow ……………….………………………………………….....12
4.1.1 Data receiving and preprocessing…………………………………..13
4.1.2 Mapping between gene expressions and pathways……….………..14
4.1.3 Find gene pairs of different co-expression in different condition……15
4.1.4 Definition for relations of gene pairs…………………….…...…….18
4.1.5 Finding different relation gene pairs……..…...…………………….19
Chapter 5 Results ……………………………………………………..……..20
5.1 Statistical results in single data ……………………………….……..20
5.2 The results in different experiment data …………………………..21
5.2.1 Distribution in the pathways we selected…………………………..23
5.3 Comparisons between co-regulated pairs and interaction pairs on
KEGG pathway……………………………..…………...………..……..23
Chapter 6 Discussion ………………………….……………………………..25
6.1 Natural kill cell mediated cytotoxicity pathway is selected by our
method……………………………………………………………..……..25
6.2 Adherens junction pathway is selected by our method…………..26
Chapter 7 Conclusion…………………………………...………………..…..27
References …………………………………………………....………...………..28
參考文獻 [1] Clemens B, et al., “How valid is single nucleotide polymorphism (SNP) diagnosis for the individual risk assessment of breast cancer?” Gynecological Endocrinology, 2007
[2] Dent R, Clemons M., “Trastuzumab after primary treatment for early stage HER2-positive breast cancer reduces recurrence.” Cancer Treat Rev. Apr;32(2):144-8, 2006
[3] Norman SA, Potashnik SL, et al., “Modifiable risk factors for breast cancer recurrence: what can we tell survivors?” J Womens Health (Larchmt). Mar;16(2):177-90. Review, 2007
[4] Pandey R, Guru RK, Mount DW., “Pathway Miner: extracting gene association networks from molecular pathways for predicting the biological significance of gene expression microarray data.” Bioinformatics. Sep 1;20(13):2156-8. Epub 2004 May 14, 2004
[5] Hee-Joon Chung, Chan Hee Park, et al., “ArrayXPath II: mapping and visualizing microarray gene-expression data with biomedical ontologies and integrated biological pathway resources using Scalable Vector Graphics.” Nucleic Acids Research, Vol. 33, Web Server issue W621–W626, 2005
[6] Kanehisa M, Goto S, Hattori M, et al., “From genomics to chemical genomics: new developments in KEGG..” Nucleic Acids Res. Jan 1;34(Database issue):D354-7, 2006
[7] Edgar R, Barrett T., “NCBI GEO standards and services for microarray data.” Nat Biotechnol. Dec;24(12):1471-2. No abstract available, 2006
[8] Regidor PA, Callies R, Regidor M, Schindler AE., “Expression of the cell adhesion molecules ICAM-1 and VCAM-1 in the cytosol of breast cancer tissue, benign breast tissue and corresponding sera.” Eur J Gynaecol Oncol.; 19(4):377-83, 1998
[9] Varker KA, et al., “Impaired natural killer cell lysis in breast cancer patients with high levels of psychological stress is associated with altered expression of killer immunoglobin-like receptors.” J Surg Res. May 1;139(1):36-44. Epub 2007 Feb 9, 2007
[10] Vazquez-Martin A, Colomer R, Menendez JA., “Protein array technology to detect HER2 (erbB-2)-induced 'cytokine signature' in breast cancer.” Eur J Cancer. 2007 May;43(7):1117-24. Epub, 2007
[11] Galliher AJ, Schiemann WP., “Src phosphorylates Tyr284 in TGF-beta type II receptor and regulates TGF-beta stimulation of p38 MAPK during breast cancer cell proliferation and invasion.” Cancer Res. Apr 15;67(8):3752-8, 2007
[12] Cowell JK, Hawthorn L., “The application of microarray technology to the analysis of the cancer genome.” Curr Mol Med. Feb;7(1):103-20. Review, 2007
[13] Liang P, Pardee AB., “Analysing differential gene expression in cancer.” Nat Rev Cancer. Nov;3(11):869-76. Review. No abstract available, 2003
[14] Frolov AE, Godwin AK, Favorova OO., “Differential gene expression analysis by DNA microarrays technology and its application in molecular oncology.” Mol Biol (Mosk). Jul-Aug;37(4):573-84. Review. Russian, 2003
[15] Gusnanto A, Calza S, Pawitan Y., “Identification of differentially expressed genes and false discovery rate in microarray studies.” Curr Opin Lipidol. Apr;18(2):187-93. Review, 2007
[16] Norman SA, Potashnik SL, Galantino ML, De Michele AM, House L, Localio AR., “Modifiable risk factors for breast cancer recurrence: what can we tell survivors?” J Womens Health (Larchmt). Mar;16(2):177-90. Review, 2007
[17] Kreike B, Halfwerk H, Kristel P, Glas A, Peterse H, Bartelink H, van de Vijver MJ., “Gene expression profiles of primary breast carcinomas from patients at high risk for local recurrence after breast-conserving therapy.” Clin Cancer Res. Oct 1;12(19):5705-12, 2006
[18] Foekens JA, et al., “Multicenter validation of a gene expression-based prognostic signature in lymph node-negative primary breast cancer.” J Clin Oncol. 2006 Apr 10;24(11):1665-71. Epub, 2006
[19] Hall P, et al., “Hormone-replacement therapy influences gene expression profiles and is associated with breast-cancer prognosis: a cohort study.” BMC Med. Jun 30;4:16, 2006
[20] Ivshina AV et al., “Genetic reclassification of histologic grade delineates new clinical subtypes of breast cancer.” Cancer Res. Nov 1;66(21):10292-301, 2006
指導教授 洪炯宗(Jorng-Tzong Horng) 審核日期 2007-7-18
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