行政院國家科學委員會補助專題研究計畫
█成果報告
□期中進度報告
基因網路和表現型特徵間關聯之統計分析
Statistical analysis of association between gene networks
and phenotypic patterns
計畫類別：V 個別型計畫 □整合型計畫
計畫編號：NSC
98-2118-M-009-004-MY3
執行期間：
98 年 8 月 1 日至 101 年 7 月 31 日
執行機構及系所：
國立交通大學統計學研究所
計畫主持人：
盧鴻興教授
共同主持人：
計畫參與人員：
第一年計畫參與人員：蔡孟原、陳亮勳、吳啟豪、許乃文、王儷芬
第二年計畫參與人員：蔡孟原、鄭宇傑、黃偉恆、吳泰言、劉耿瑋
第三年計畫參與人員：蔡孟原、鄭宇傑、魏裕中、翁俊哲、吳宜靜、
周孟穎、陳麗安、李念錡、楊家瓏
成果報告類型(依經費核定清單規定繳交)：完整報告
本計畫除繳交成果報告外，另須繳交以下出國心得報告：
█赴國外出差或研習心得報告
□赴大陸地區出差或研習心得報告
□出席國際學術會議心得報告
□國際合作研究計畫國外研究報告
處理方式：
除列管計畫及下列情形者外，得立即公開查詢
□涉及專利或其他智慧財產權，□一年□二年後可公開查詢
中 華 民 國 101 年 10 月 30 日
行政院國家科學委員會專題研究計畫期中成果報告
基因網路和表現型特徵間關聯之統計分析
Statistical analysis of association between gene networks and
phenotypic patterns
計畫編號：NSC 98-2118-M-009-004-MY3
執行期限：98 年 8 月 1 日至 101 年 7 月 31 日
主持人：國立交通大學統計學研究所盧鴻興教授
第一年計畫參與人員：蔡孟原、陳亮勳、吳啟豪、許乃文、王儷芬
第二年計畫參與人員：蔡孟原、鄭宇傑、黃偉恆、吳泰言、劉耿
第三年計畫參與人員：蔡孟原、鄭宇傑、魏裕中、翁俊哲、吳宜靜、
周孟穎、陳麗安、李念錡、楊家瓏
一、中文摘要 高傳出技術產生大量異質性的生醫數 據，可用來了解大規模基因網路與高維度 表現型特徵之關聯。我們計劃藉系統生物 學的觀點，結合布林網路、貝氏網路、核 方法、維度縮減等技術去發展統計學習方 法。 首先我們計劃透過基因型與表現型的 相關結構，辨識哪些具共同表現型的基 因。因愈多共同表現型的基因應愈傾向於 影響彼此的生物功能，交互作用的強度也 將幫助計算基因網路中表現型的相似值。 我們將透過統計方法和生物資訊的整合， 深入了解表現型相關性和基因網路特徵辨 識。 另外具共同基因的表現型亦可被視為 一種相似的特徵，可能是由相同的機制所 造成。因此，計劃發展在基因網路結構下 的量度工具，構造一個分析基因型及表現 型之相依性的系統性架構。 依此方法，我們能執行任意疾病和組 織型態所組成的特定網路分析。例如，能 用此法以協助發現及開發高效與低副作用 的藥物、治療。藉生醫研究中的基因網路 和表現型相關性，我們將在長期計畫中進 而深入發展統計方法，有助於疾病的治療 和生物研究的分析。 關鍵詞：統計學習、布林網路、貝氏網路、 核方法、維度縮減、高維度資料、表現型 相關性、基因網路、機制分析、特徵辨識、 系統生物、藥物研發、副作用。 AbstractHigh throughput techniques have generated massive amount and heterogeneous types of biological and medical data that are in the immediate need of statistical analysis for association between structure in the large scale gene networks and the trait patterns of phenotypes in high dimensional space. In this long term project, we plan to develop statistical learning methods for the analysis and integration of different kinds of data by systematic approaches of network biology, including Boolean networks, Bayesian networks, kernel methods, dimension reduction and related techniques. Specifically, we will consider the following topics to investigate during this project.
Firstly, we plan to identify gene similarities based on their shared phenotypic features by the structure of association between genes and phenotypes. The reasoning behind this approach of analysis is that genes sharing more similar phenotypes shall have a stronger tendency for functional interactions in biology which will in turns provide the usefulness of phenotype similarity values in gene network analysis. Statistical measures of phenotypic association of genes will be investigated. The relationship of phenotypic association to the
pattern recognition of gene networks will be explored by statistical methods together with the integration of biological information.
Alternatively, phenotypes that are associated with common genes can be regarded as similar traits that are likely to be the outcomes of similar pathways consisted of these common genes from the perspective of biological systems. Hence, we plan to develop statistical measures for the similarity of phenotypes based on the association patterns of genotypes and phenotypes with the structure of gene networks behind the analysis steps. This will provide a systematic framework to analyze the interdependence of genotypes and phenotypes through biologic network structure from the perspective of systems biology with state-of-arts techniques in statistical analysis.
With these analysis measures and methods, we can perform disease-specific and tissue-specific network analysis for any combination of disease and tissue types. For example, we can use this analysis framework to assist the discovery and development in designing high efficacy and low side-effect drugs and treatments. Other applications are also possible and will be investigated. The statistical methods developed in this long term project will be very useful for the treatment of human diseases and analysis of biologic studies through the association of gene networks and trait phenotypes in biologic and medical researches during collaborative studies and follow-up investigation.
Keywords: statistical learning, Boolean
networks, Bayesian networks, kernel methods, dimension reduction, high dimensional data, phenotypic association, gene network, pathway analysis, pattern recognition, systems biology, drug discovery, side-effect.
二、緣由與目的
There exist interdependence relationships between genotypes and phenotypes from the perspectives of biological networks and biochemical pathways. Diseases are resulted
from several genes mutation or abnormal expression. The exploring of these relationships requires tremendous biologic and medical experiments which cost lots of money and time. With growing scale of biological and medical data by high throughput techniques, we want to develop a highly efficient method through the statistical analysis based on the association networks of genotypes and phenotypes. It will provide a systematic framework for investigating the interdependence of genes and phenotypic features. With the research and development of analysis methods in this long term project, it can also speed up the research speed of drug discovery and enhance the reliability of new drugs and therapies. This method will be of great use in many biological and medical studies. In this long term project, we plan to develop statistical learning methods for the network analysis of biological studies in yeast and medical investigation of human diseases with collaborators.
三、結果與討論
本三年期計畫在計畫期間己發表論文 如下。
1. Emerson, J. J., Hsieh, L.-C., Sung, H.-M., Wang, T.-Y., Huang, C.-J., Lu, H. H.-S., Lu, M.-Y. J., Wu, S.-H., and Li, W. H., "Natural selection on cis and trans regulation in yeasts", Genome Research, 20, 826-836. 2010.
2. Deng, L.-Y. , Lu, H. H.-S., and Chen, T.-B., "64-Bit and 128-bit DX random number generators", Computing, 89, 1, 27-43. 2010.
3. Cheng, J. H., Wang, Y., Chen, P. Y., Chen, T.-B., Chen, C.-J., Li, G.-C., and Lu, H. H.-S., "Mine Barcode of Life: Information Visualization and Fusion for the Environment and Society" , International Journal of Systems and Synthetic Biology, 1(1), 63-70. 2010.
4. Lu, H. H.-S., and Wu, H. M., "Visualization, Screening, and Classification of Cell Cycle-Regulated Genes in Yeast", International Journal of Systems and Synthetic Biology, 1(2), 185-198, 2010.
5. Wang, H., Lu, H. H.-S., Chueh, T.-H., "Constructing Biological Pathways by a
Two-Step Counting Approach", PLoS ONE 6(6): e20074, 2011.
6. Deng, L.-Y. , Shiau, J.-J. H., and Lu, H. H.-S., "Large-order multiple recursive generators with modulus $2^{31}-1$", INFORMS Journal on Computing, Published online before print, October 17, 2011.
7. Deng, L.-Y. , Shiau, J.-J. H., and Lu, H. H.-S., "Efficient computer search of large-order multiple recursive pseudo-random number generators", Journal
of Computational and Applied Mathematics, 236, 3228– 3237, 2012.
8. Chiang, S, Swamy, K. B., Hsu, T. W., Tsai, Z. T., Lu, H. H.-S., Wang, D., Tsai, H. K., "Analysis of the association between transcription factor binding site variants and distinct accompanying regulatory motifs in yeast", Gene. 2012 Jan 10;491(2):237-45. Epub 2011 Sep 16.
9. Chueh, T.-H., and Lu, H. H.-S., "Inference of Biological Pathway from Gene Expression Profiles by Time Delay Boolean Networks", PLoS ONE 7(8): e42095, 2012. 四、計畫成果自評 由上述的報告中，可以發現我們的研 究內容與原計畫相符，達成預期的目標。 我們將進一步將完成的技術報告投稿到學 術期刊發表，並進一步將這些技術應用到 實際的資料，提供更正確和有效的統計分 析。因此，本計畫的研究除了在學術上分 析方法的突破，也同時具備應用的價值。 五、參考文獻
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