| Related citation: | Maozu Guo,Shuang Cheng,Chunyu Wang,Xiaoyan Liu,Yang Liu.Prediction of Potential Disease-Associated MicroRNAs Based on Hidden Conditional Random Field[J].Journal of Harbin Institute Of Technology(New Series),2018,25(1):57-66.DOI:10.11916/j.issn.1005-9113.16139. |
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| Author Name | Affiliation | | Maozu Guo | School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Beijing Key Laboratory for Research on Intelligent Processing Method of Building Big DataBeijing University of Civil Engineering and Architecture , Beijing 100044, China | | Shuang Cheng | School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China
Institute of Materials, China Academy of Engineering Physics, Mianyang 621700,Sichuan，China | | Chunyu Wang | School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China | | Xiaoyan Liu | School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China | | Yang Liu | School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China |
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| Abstract: |
| MicroRNAs (miRNAs) are reported to be associated with various diseases. The identification of disease-related miRNAs would be beneficial to the disease diagnosis and prognosis. However, in contrast with the widely available expression profiling, the limited knowledge of molecular function restrict the development of previous methods based on network similarity measure. To construct reliable training data, the decision fusion method is used to prioritize the results of existing methods. After that, the performance of decision fusion method is validated. Furthermore, in consideration of the long range dependencies of successive expression values, Hidden Conditional Random Field model (HCRF) is selected and applied to miRNA expression profiling to infer disease-associated miRNAs. The results show that HCRF achieves superior performance and outperforms the previous methods. The results also demonstrate the power of using expression profiling for discovering disease-associated miRNAs. |
| Key words: expression profiling hidden conditional random field miRNA disease association network |
| DOI：10.11916/j.issn.1005-9113.16139 |
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| Descriptions in Chinese: |
| 基于隐条件随机场的miRNA-疾病关联预测方法 郭茂祖1，2，3，程爽1，4，王春宇1，刘晓燕1，刘扬1 （1.哈尔滨工业大学，计算机科学与技术学院，哈尔滨 150001； 2.北京建筑大学 电气与信息工程学院， 北京 100044； 3. 北京建筑大学，建筑大数据智能处理方法研究北京市重点实验室，北京 100044； 4. 中国工程物理研究院，材料研究所，四川 绵阳 621700） 创新点说明：1）考察miRNA功能的时序特异性； 2）采用加入隐变量的条件随机场模型HCRF，识别miRNA表达值序列中具有生物意义的子序列。 研究目的： miRNA的功能异常是导致疾病产生的重要原因，研究miRNA-疾病的关联能够为临床提供诊断依据和治疗方向。 研究方法： 选取了大量可用的、且能体现miRNA功能动态性的miRNA表达谱数据。利用决策融合思想，为表达谱中的miRNA分配可靠的类别标签。采用加入隐变量的条件随机场模型HCRF，识别miRNA表达值序列中具有生物意义的子序列。 结果： 与以往预测方法比较， HCRF模型的AUC值高于其他三种方法的AUC值。将HCRF模型与HMM、CRF模型进行比较，HCRF模型的分类结果明显优于另外两种方法。 结论： 该方法为后续生物实验提供可靠的与疾病关联的miRNA候选，揭示了miRNA表达谱数据在miRNA-疾病关联预测领域上的广泛应用前景。 关键词：表达谱；隐条件随机场；miRNA-疾病关联；网络 |
