Transcriptional Regulatory Relationship Mining Based On Microarray Data And Sequence Features

The properties of a biological system include system structures, system dynamics, control method and design method. Biological systems can be depicted as various biological networks, such as metabolic networks, signal transduction networks, regulatory networks, and so on.As one basic process of biological activity, gene regulation plays a dominant role in the biological system. By analyzing gene regulation via experimental and bioinformatic method, we could extract the structure features of a biological system. We can also identify the complex regulatory relationships, uncover the regulatory patterns in the cell, and gain the systematic view of the biological process by the gene regulatory network analyses.With the deeper development and broader application of the high-throughput techniques in the research of life science, microarray data emerges massively and rapidly, which makes the research on the gene regulatory network reconstruction become a hotspot.Many algorithms have been developed to construct gene regulatory networks based on microarray data. Unfortunately, most of these works focus on a specific biological or pathology problem by mining the precise wet-experiment data. Besides, intuitive parameters could not be produced by most models. One remaining problem is whether there are some simple but potential basic characteristics of microarray to be uncovered.Aiming to overcome these shortcomings, we integrated multiple parameters to characterize the expression profile features and combined them with other biological evidences. Meanwhile, we extracted sequence features of regulatory elements without using the prior knowledge. Combining several different evidences, we developed a new approach to predict the regulatory relationship. Our research is based on the model organism Saccharomyces Cerevisiae. The first step is to select features to measure expression profiles. Then we extract sequences features of the expression elements. Finally, a comprehensive method is constructed to infer the gene regulatory relationships, which expand our knowledge on biological system.Based on the expression correlation, the expression level variation and the vectors derived from microarray datasets, we first introduced several novel parameters to describe the characters of regulating gene pairs. Subsequently, we used the na?ve Bayesian network model to integrate these features and the functional co-annotation which lies between the transcription factors and their target genes. This model is proved to have higher efficacy than the previous individual feature model. With this model and based on the time-delay character of time-series microarray datasets, we can predict the accuracy and coverage of existence and direction of the regulatory relationship respectively. This helps to build an integrated prediction and evaluation system.Parametric approach has both pros and cons. A series of parameters may be intuitive indexes. However, information extraction may cause information loss or misleading. Besides, noise included in microarray may disturb the results. So we chose machine learning approach instead of manual selection. We introduced an expression pattern index FAB . With this index, we extracted the main features of expression level and excluded interference elements via Principle Component Analysis method. This approach is proved to be able to improve the accuracy of regulatory relationship prediction.Not all the essential genes can be detected by the knock-out or knock-down experiments because of the expression diversity. In this case, sequence features analysis should be considered. We used dimension reducing algorithm to extract sequence features of the regulatory elements. With the help of prior knowledge, we adopted support vector machine-based method to find the sequence feature of regulatory elements. The results show that it is feasible to mine regulatory relationships based on sequence feature. The accuracy is stable when the clustering methods and the clustering character are changed. And the parameters extracted from tensor analysis have also been verified to be acceptable. This approach might be a suitable complement to microarray-based approach. Unlike other global expression profiles computing methods, our approach is mainly based on several novel parameters, which could be intuitive indicators. Combining some prior knowledge, our approach could improve the accuracy of regulatory relationship mining. The regulatory element feature selection result shows its advantages on mining the regulatory relationship by using the sequence feature.To summarize, we firstly proposed a novel parametric approach to infer gene regulatory relationship from microarray datasets. Then we used machine learning method to extract expression feature and mine the regulatory relationship. Finally we developed a new strategy for gene regulatory relationship mining based on sequence features analysis, which can greatly improve the sensitivity and coverage of transcriptional regulatory mining.With the development of the microarray technology, our approaches are promising to bring more contribution to the regulatory network research as well as the genome type analysis in the clinical diagnosis.