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复杂疾病的生物信息学研究
Alternative TitleBioinformatics for Complex Diseases
刘冰
Subtype工学博士
Thesis Advisor蒋田仔
2007-05-22
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline模式识别与智能系统
Keyword复杂疾病 生物信息学 基因芯片 多特征融合 神经网络集成 复杂脑基因网络 系统生物学 Complex Disease Bioinformatics Microarray Feature Combination Ensemble Neural Network Complex Brain Gene Network Systems Biology
Abstract人类各种常见疾病都属于复杂疾病。它们不是由单一基因所决定的,而是由多基因、多因素、遗传和环境共同作用的结果。因此,对于复杂疾病的研究来说,孟德尔遗传疾病的研究模式已经远远不能满足需要,对复杂疾病的理解将是21世纪医学科学中一个巨大的挑战。自人类基因组计划完成之后,基于人类基因组计划的研究可以概括为三个主题:基因组学与生物学、基因组学与健康、基因组学与社会。本文着眼于其中的基因组学与健康这一主题,重点在于复杂疾病的生物信息学研究。这一研究的具体目标是,基于与人类复杂疾病密切相关的大规模基因组学、蛋白质组学以及遗传学数据,开发有效的生物信息学方法,用来阐明人类基因和蛋白质的结构、功能、相互作用,以及与各种人类疾病之间的关系。而最终目标则是,发展以系统生物学的观点研究健康与疾病的新方法,从而加深对复杂疾病病理机制的理解与认识,以期为复杂疾病的诊断与治疗提供一定的线索。本文的工作主要体现在以下两个方面: 1. 基于基因芯片的复杂疾病分类:基于大规模基因芯片数据,我们可以对各种复杂疾病进行分类与有效预测研究。目前,各种复杂疾病模式发现与分类的方法,多是基于某一种特征选择和分类方法。有别于此,本文提出了一种基于多特征融合和神经网络集成的方法用于芯片的分类。我们通过神经网络集成来融合多种特征信息,从不同侧面反映出模式的本质,从而一定程度上能够充分利用基因芯片实验所提供的信息。我们用多套不同的复杂疾病芯片数据检验该算法模型,均取得了比已有方法显著提高的分类效果。 2. 基于系统生物学的复杂疾病基因发现:我们从系统生物学的角度,通过结合各种相关基因组学、蛋白质组学以及遗传学信息,提出了基于复杂脑基因网络预测脑疾病基因的方法。首先,我们基于贝叶斯准则,集成多种不同的相关生物学数据集,以达到充分利用各种已有信息的目的,首次构建出了一个人脑特异性的复杂基因网络。然后,基于这一复杂脑基因网络,我们发展了从网络中寻找复杂脑疾病相关基因的有效方法,从而可以快速大规模地发现复杂脑疾病的相关基因。最后,我们将这一方法应用到阿尔茨海默氏病相关基因的发现,取得了较好的预测效果,为相关遗传学研究提供了有价值的参考信息。我们的这一研究方法,为深入理解复杂脑疾病的病理机制开辟了一个新的研究思路。
Other AbstractMany common human diseases are complex diseases. They are caused by not one single gene, but the interactions between large numbers of genes and environments. So the methods for Mendelian genetic disease study are not suitable for the complex disease very well. Undstanding the molecular mechanisms of complex diseases will be a great challenge in the medical and biological areas of the 21st century. With the end of Human Genome Project (HGP), we entered the postgenomic era. The studies based on HGP can be classified into three subjects: genome and biology, genome and health, genome and society. In this dissertation, we focus on the subject of genome and health.That is, from the point of bioinformatics for complex diseases, we aim to avail of complex disease related genomic, proteomic and genetic datasets, and develop effective bioinformtic methods to clarify the structure, function, and interactions of human genes and proteins, and the relationships between them and complex disease. The ultimate goal of our study is to develop the systematic methods for complex diseases, so as to understand the molecular mechanisms of complex diseases, and to provide valuable clues from their diagnosis and therapy. The main contents and contributions of this dissertation are as follows: 1. The classification of complex diseases based on microrray datasets: Most methods of pattern recognition and classification based on large-scale microarray datasets use one individual feature selection and classification method, we proposed a combinational feature selection and ensemble neural network method for classification of gene expression data. By combining various features, we can make full use of all available information, and can significantly improve the accuracy and stability of classification. On a wide range of published datasets, our method performs better, or is at least comparable to, the current best methods of our knowledge. 2. Complex disease gene finding based on systems biology: From the view of systems biology, we developed a new method to explore candidate genes for human brain diseases based on a brain-specific gene network. By integrating various diverse genomic and proteomic datasets based on Bayesian theory, we firstly constructed a complex human brain-specific gene network. Then we developed an effective method to find brain disease related gene subnetwork from the entire network. When this method is applied to predict Alzheimer’s disease related genes, the results show that this method can provide many valuable clues for other studies.
shelfnumXWLW1107
Other Identifier200418014628020
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/5966
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
刘冰. 复杂疾病的生物信息学研究[D]. 中国科学院自动化研究所. 中国科学院研究生院,2007.
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