Regulatory genes prediction with microarray data and ontology

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Title:	Regulatory genes prediction with microarray data and ontology
Other Titles:	使用微陣列資料與本體論於基因調控關係預測
Authors:	楊朝勛;Yang, Chao-Hsun
Contributors:	淡江大學資訊工程學系博士班許輝煌
Keywords:	基因微陣列;調控基因預測;遺失值填補;動態時間配置法;基因本體論;Microarray Time-Series Data;Gene Regulation Prediction;Missing Value Imputation;Dynamic Time Warping;Gene Ontology
Date:	2011
Issue Date:	2011-06-16 22:07:42 (UTC+8)
Abstract:	基因微陣列近年來被大量應用在生物相關研究上。生物學家可藉由基因微陣列實驗所得之大量實驗結果，來進行後續的研究與分析。然而，如何在大量微陣列實驗資料中找出具有調控關係的基因組，是微陣列資料分析中的一重要研究議題。現今由文獻中所提出之數種方法，皆有其限制與缺點。另一方面，許多基因微陣列實驗所得實驗資料，經常包含許多不存在的欄位，這些欄位被稱為遺失值。遺失值的產生，可能源自於許多原因，例如:該實驗欄位反應不明顯、實驗儀器誤差、或是人為疏失等。由於許多用來對微陣列資料進行後續分析的演算法，都需要較完整的基因微陣列資料。因此，這些遺失值必須藉由有效的方法來加以估算與填補。在此論文中，我們提出一個可有效預測調控基因的方法。此預測方法是基於我們所設計的兩基因之間距離測量法。該距離測量法，結合了動態時間配置法和基因本體論資訊的應用。動態時間配置法，可有效計算兩序列資料之間的距離；而基因本體論結構中，對於每一個已知基因，皆有關於該基因屬性的描述與註解資料。透過對這些用來描述基因特性之資料的量化與評估，我們可以估算兩基因間在生物意義上的距離，或者相似度。因此，我們所設計的兩基因之間距離測量法，不但將基因微陣列時間序列資料中之基因表現值列入考量，同時也參考了與這些基因相關之基因本體論描述資訊。除此之外，我們也提出一個填補方法來有效估算與填補基因微陣列資料中所包含之遺失值。此遺失值填補法將我們所設計的兩基因之間距離測量法與K個相鄰節點法進行結合，來估算並填補基因微陣列資料中的遺失值。由我們的實驗結果顯示，比起其他相關文獻所提出的方法，我們的遺失值填補法能夠更有效的對於基因微陣列中的遺失值進行填補與估算。因此我們先以所設計的遺失值填補法將基因微陣列中的遺失值填補後，再以我們的調控基因預測法來預測在大量基因微陣列資料中，可能存在哪些調控基因組。由調控基因預測的實驗結果也顯示，比起其他方法，我們所提出之調控基因預測法，能夠更有效的找出在所使用的基因微陣列資料中已知的調控基因組，進而提供可能具有調控關係的候選基因組。我們所提出之遺失值填補法與調控基因預測法，將有助於後續基因微陣列資料之分析與研究。 Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. However, microarray gene expression data often contain multiple missing expression values due to many reasons. Effective methods to impute these missing values are needed since many algorithms for microarray data analysis require a complete matrix of gene expression values. In addition, selecting informative genes from microarray gene expression data is essential while performing data analysis on these large amounts of data. To fit this need, a number of methods were proposed from various points of view. However, most existing methods have their limitations and disadvantages. In this dissertation, we propose a novel approach to predict potential regulatory gene pairs through our distance measurement that estimates the distances between gene pairs effectively. The distance measurement is based on the dynamic time warping (DTW) algorithm and the well-defined gene ontology (GO) structure for genes or proteins. GO contains definition (annotations) for genes that describe the biological meanings of them. The semantic distance of two genes within biological aspect can be measured by performing proper quantitative assessments of their corresponding GO annotations. Our distance measurement takes both DTW distances of expression values and GO semantic distances of gene pairs into consideration. Besides, we also propose a novel missing value imputation approach by combining our distance measurement with the k-nearest neighbor (KNN) method. Experimental results show that our missing value imputation approach outperforms other major methods in terms of the commonly-used assessment. After missing values in microarray time series raw data are estimated effectively with our imputation approach, we then perform our gene regulation prediction approach. According to experimental results, our approach can discover more known regulatory gene pairs compared with other methods. Researches on microarray time series data can hence be improved and facilitated with our approaches.
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