复杂疾病相关的非编码RNA识别模型及算法研究

多项研究证实，非编码RNA 具有多种复杂的调控功能，其调节异常往往是多种复杂疾病发病的重要原因。识别与复杂疾病相关的非编码RNA，对理解复杂疾病发病机理，确定复杂疾病相关的标记物，研发药物以及促进临床医疗，改善全人类的健康水平等具有极为重要的意义，是目前生物医学领域的重点和热点问题。 本项目将通过收集、整合相关研究领域中海量的多类型数据，构建非编码RNA和复杂疾病的关联网络，通过对网络的拓扑分析，综合运用机器学习、概率统计和优化等方法构建识别复杂疾病相关的非编码RNA的数学模型。针对特殊复杂病种，利用所构建的预测模型识别潜在的与其相关的非编码RNA，并通过实验手段进行验证，这将对揭示非编码RNA在复杂疾病的发生发展进程中的作用机制，解析一些非编码RNA的表达特征和生物学功能，为人类最终攻克如肿瘤、心脑血管疾病、糖尿病等复杂疾病提供理论基础。

Many researches have confirmed that noncoding RNA has many complicated regulatory function, and dysregulation of them would lead to various kinds of complex diseases. Identifying noncoding RNA associated with complex disease would be greatly beneficial to promote the understanding of the pathogenesis of complex diseases, determine markers related complex diseases, develop drugs and improve the public health level, which is the hot and focus problem in bio-medical field at present. Our project would collect and integrate a large number of different kinds of data, and construct association network between complex disease and noncoding RNA. After topology analysis on the association network, we will build mathematical model through comprehensive application of several kinds of methods, such as machine learning, probability and statistics, optimization and so on. For specific complex diseases, we would identify its associated noncoding RNAs and verify them utilizing bioexperiment. The model constructed in our project would provide theoretical basis for uncovering noncoding RNAs interaction mechanism in the progress of complex disease, analyzing expression features and biological functions of some noncoding RNAs, and seeking a cure for human complex diseases such as cancer, cardiovascular and cerebrovascular diseases, diabetes and so on.