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Identifying mirna-disease association based on integrating mirna topological similarity and functional similarity

Quantitative Biology 2019年第3期7卷 202-209页

作者： Qingfeng Chen Zhao Zhe Wei Lan Ruchang Zhang Zhiqiang Wang Cheng Luo Yi-Ping Pheobe Chen School of Computer Electronic and InformationGuangxi UniversityNanning 530004China The State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources Guangxi UniversityNanning 530004China Xingjian College of Science and Liberal Arts Guangxi UniversityNanning 530004China Department of Computer Science and Computer Engineering La Trobe UniversityMelboumeVic 3086Australia

Background:MicroRNAs(mirnas)are a significant type of non-coding RNAs,which usually were encoded by endogenous genes with about?22 nt *** biological experiments have shown that mirnas have close associations with various human *** traditional experimental methods achieve great successes in mirna-disease interaction identification,these methods also have some ***,it is necessary to develop computational method to predict mirna-disease ***:Here,we propose a computational framework(MDVSI)to predict interactions between mirnas and diseases by integrating mirna topological similarity and functional ***,the CosRA index is utilized to measure mirna similarity based on network topological ***,in order to enhance the reliability of mirna similarity,the functional similarity and CosRA similarity are integrated based on linear weight ***,the potential mirna-disease associations are predicted by using recommendation *** addition,in order to overcome limitation of recommendation method,for new disease,a new strategy is proposed to predict potential interactions between mirnas and new disease based on disease functional ***:To evaluate the performance of different methods,we conduct ten-fold cross validation and de novo test in experiment and compare MDVSI with two the-state-of-art *** experimental result shows that MDVSI achieves an AUC of 0.91,which is at least 0.012 higher than other compared ***:In summary,we propose a computational framework(MDSVI)for mirna-disease interaction *** experiment results demonstrate that it outperforms other the-state-of^the-art *** study shows that it can effectively identify potential mirna-disease interactions.

关键词： mirna-disease association CosRA index mirna functional similarity recommendation method

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Prediction of Potential disease-Associated MicroRNAs Based on Hidden Conditional Random Field

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Journal of Harbin Institute of Technology(New Series) 2018年第1期25卷 57-66页

作者： Maozu Guo Shuang Cheng Chunyu Wang Xiaoyan Liu Yang Liu 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 Data( Beijing University of Civil Engineering and Architecture) Beijing 100044China Institute of Materials China Academy of Engineering PhysicsMianyang 621700SichuanChina

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.

关键词： expression profiling hidden conditional random field mirna-disease association network

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