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Human disease MiRNA inference by combining target information based on heterogeneous manifolds

JOURNAL OF BIOMEDICAL INFORMATICS 2018年 80卷 26-36页

作者： Ding, Pingjian Luo, Jiawei Liang, Cheng Xiao, Qiu cao, buwen Hunan Univ Coll Comp Sci & Elect Engn Changsha 410083 Hunan Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Shandong Peoples R China

The emergence of network medicine has provided great insight into the identification of disease-related molecules, which could help with the development of personalized medicine. However, the state-of-the-art methods could neither simultaneously consider target information and the known miRNA-disease associations nor effectively explore novel gene-disease associations as a by-product during the process of inferring disease-related miRNAs. Computational methods incorporating multiple sources of information offer more opportunities to infer disease-related molecules, including miRNAs and genes in heterogeneous networks at a system level. In this study, we developed a novel algorithm, named inference of Disease-related MiRNAs based on Heterogeneous Manifold (DMHM), to accurately and efficiently identify miRNA-disease associations by integrating multi-omits data. Graph-based regularization was utilized to obtain a smooth function on the data manifold, which constitutes the main principle of DMHM. The novelty of this framework lies in the relatedness between diseases and miRNAs, which are measured via heterogeneous manifolds on heterogeneous networks integrating target information. To demonstrate the effectiveness of DMHM, we conducted comprehensive experiments based on HMDD datasets and compared DMHM with six state-of-the-art methods. Experimental results indicated that DMHM significantly outperformed the other six methods under fivefold cross validation and de novo prediction tests. Case studies have further confirmed the practical usefulness of DMHM.

关键词： miRNA-disease associations Heterogeneousnetwork Manifold learning Graph regularization Target information

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An Efficient Methodology for License Plate Localization and Recognition with Low Quality Images

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Sainan Xiao Wangdong Yang buwen cao Honglie Zhou Chenjun He College of Information and Electronic Engineering Hunan City University Yiyang 413000 Hunan Province China

It is challenging to find an effective license plate detection and recognition method due to the different conditions during the image acquisition phase. This paper aims to develop a new accurate and efficient method based on color difference and SVM recognition model that yields better performance for vehicle images under low quality. The proposed method is tested with 200 images which involve many difficult conditions, such as low resolution, night-lighting, dirt, complicated background, and distortion problems. The experimental results demonstrate very satisfactory performance for license plate detection in terms of speed and accuracy and are better than the existing methods like edge detection or HSV color conversion method. The overall probability of localization is close to 100%, with a false recognition rate of 2%.

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Ni Li Manman Peng buwen cao Kenli Li Keqin Li College of Computer Science and Electric Engineering Hunan University Hunan Changsha 410082 China College of Information and Electronic Engineering Hunan Yiyang 413000 China Engineering Research Center of DongTing Lake regional ecological environment intelligent monitoring and disaster prevention and Mitigation Hunan Yiyang 413000 China Key Laboratory of city computing and IoT Hunan City University Hunan Yiyang 413000 China Department of Computer science State University of New York New Paltz NY 12561 USA

Spectral clustering is one of the most popular clustering approaches. Despite its good performance and strong theoretical supports, it is limited to high complexity of the graph Laplacian similarity matrix construction and eigen-decomposition problems. Recently, deep learning has been successfully adopted in graph representation. In the paper, we jointly learn the manifold graph construction and non-linear low-dimension mapping of the graph. In addition, we theoretically proved that our model according with spectral clustering theory. Meanwhile, we use the proposed non-linear coders as the building blocks to formulate a deep structure to further refine features of layer wise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both clustering accuracy and normalized mutual information( NMI )

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Ru doping induces the construction of a unique core-shell microflower self-supporting electrocatalyst for highly efficient overall water splitting

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DALTON TRANSACTIONS 2021年第39期50卷 13951-13960页

作者： Ye, Lei Zhang, Yeqing Guo, buwen cao, Duanlin Gong, Yaqiong North Univ China Sch Chem Engn & Technol Taiyuan 030051 Shanxi Peoples R China Xizang Minzu Univ Sch Management Xianyang 712082 Peoples R China Xiamen Univ State Key Lab Phys Chem Solid Surfaces Xiamen 361005 Fujian Peoples R China

Since the large reaction energy barrier caused by multi-step electron transfer processes of the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) gravely restricts the practical application of electrocatalytic water splitting, it is urgent to develop a dual-functional electrocatalyst which can effectively reduce the reaction energy barrier and actually speed up the reaction. Herein, the Ru species are doped into the complex of magnetite and FeNi-layered double hydroxide by a one-step oil bath method, and a self-supporting binder-free bifunctional electrocatalyst was synthesized on the surface of iron foam (named Ru-Fe3O4@FeNi-LDH/IF). The unique 3D core-shell microflower structure of Ru-Fe3O4@FeNi-LDH/IF, the combination of active ingredient and conductive substrate, together with the doping of Ru may immensely provide a large number of active sites, adjust the electronic structure, accelerate electron transfer, and thus greatly improve the electrocatalytic activity and durability. It is worth mentioning that when Ru-Fe3O4@FeNi-LDH/IF is used as the anode and cathode for overall water splitting, only 1.52 V battery voltage can generate a current density of 10 mA cm(-2), and also maintain a prominent stability for at least 36 hours. This work provides a feasible strategy for heteroatom-doping LDH as a bifunctional electrocatalyst.

关键词： Magnetite

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Collective Prediction of Disease-Associated miRNAs Based on Transduction Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2017年第6期14卷 1468-1475页

作者： Luo, Jiawei Ding, Pingjian Liang, Cheng cao, buwen Chen, Xiangtao Hunan Univ Coll Comp Sci & Elect Engn Changsha 410083 Hunan Peoples R China Shandong Normal Univ Coll Informat Sci & Engn Jinan 250000 Shandong Peoples R China

The discovery of human disease-related miRNA is a challenging problem for complex disease biology research. For existing computational methods, it is difficult to achieve excellent performance with sparse known miRNA-disease association verified by biological experiment. Here, we develop CPTL, a Collective Prediction based on Transduction Learning, to systematically prioritize miRNAs related to disease. By combining disease similarity, miRNA similarity with known miRNA-disease association, we construct a miRNA-disease network for predicting miRNA-disease association. Then, CPTL calculates relevance score and updates the network structure iteratively, until a convergence criterion is reached. The relevance score of node including miRNA and disease is calculated by the use of transduction learning based on its neighbors. The network structure is updated using relevance score, which increases the weight of important links. To show the effectiveness of our method, we compared CPTL with existing methods based on HMDD datasets. Experimental results indicate that CPTL outperforms existing approaches in terms of AUC, precision, recall, and F1-score. Moreover, experiments performed with different number of iterations verify that CPTL has good convergence. Besides, it is analyzed that the varying of weighted parameters affect predicted results. Case study on breast cancer has further confirmed the identification ability of CPTL.

关键词： Collective prediction miRNA disease transduction learning miRNA-disease network

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Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk

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IEEE ACCESS 2017年 5卷 24032-24039页

作者： Li, Guanghui Luo, Jiawei Xiao, Qiu Liang, Cheng Ding, Pingjian cao, buwen East China Jiaotong Univ Sch Informat Engn Nanchang 330013 Jiangxi Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Shandong Normal Univ Coll Informat Sci & Engn Jinan 250000 Shandong Peoples R China Hunan City Univ Coll Informat & Elect Engn Yiyang 413000 Peoples R China

Recently, increasing experimental studies have shown that microRNAs (miRNAs) involved in multiple physiological processes are connected with several complex human diseases. Identifying human disease-related miRNAs will be useful in uncovering novel prognostic markers for cancer. Currently, several computational approaches have been developed for miRNA-disease association prediction based on the integration of additional biological information of diseases and miRNAs, such as disease semantic similarity and miRNA functional similarity. However, these methods do not work well when this information is unavailable. In this paper, we present a similarity-based miRNA-disease prediction method that enhances the existing association discovery methods through a topology-based similarity measure. DeepWalk, a deep learning method, is utilized in this paper to calculate similarities within a miRNA-disease association network. It shows superior predictive performance for 22 complex diseases, with area under the ROC curve scores ranging from 0.805 to 0.937 by using five-fold cross-validation. In addition, case studies on breast cancer, lung cancer, and prostatic cancer further justify the use of our method to discover latent miRNA-disease pairs.

关键词： Deep learning disease-related microRNAs microRNA-disease association similarity measure

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基于FPGA的拉曼分布式光纤测温系统数据采集处理模块设计

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激光与光电子学进展 2024年第17期61卷 107-115页

作者：陈洋乔丽君曹康怡范卜文郭浩森秦嘉辰李健张明江太原理工大学新型传感器与智能控制教育部重点实验室山西太原030024 太原理工大学物理学院山西太原030024 太原理工大学电子信息与光学工程学院山西太原030024

传统拉曼分布式光纤测温系统中,系统的空间分辨率、温度精度、传感距离和信噪比等重要指标受采集处理模块的采样速率、信号量化位数、板载内存和数据处理能力性能制约,导致无法发挥其全部性能。本文设计了一种基于现场可编程门阵列(FPGA... 详细信息

传统拉曼分布式光纤测温系统中,系统的空间分辨率、温度精度、传感距离和信噪比等重要指标受采集处理模块的采样速率、信号量化位数、板载内存和数据处理能力性能制约,导致无法发挥其全部性能。本文设计了一种基于现场可编程门阵列(FPGA)的长距离拉曼分布式光纤测温系统数据采集处理模块,采用AXI4总线分时突发读写DDR3存储器技术实现超长数据实时累加平均处理,通过用户数据报协议(UDP)实现FPGA与上位机之间的数据传输,并且使用上位机对接收的数据进行温度解调和显示。实验结果表明,所提出的数据采集处理模块能够支持89.6 km的光纤信号采集,在10万次累加平均处理后,测温信号信噪比达到43.65 dB,测温精度稳定在±0.21℃。该数据采集处理模块能够进一步推动拉曼分布式光纤测温系统的应用与发展。

关键词：拉曼分布式光纤测温系统现场可编程门阵列 DDR3 AXI4 累加平均处理

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PCE-FR: A Novel Method for Identifying Overlapping Protein Complexes in Weighted Protein-Protein Interaction Networks Using Pseudo-Clique Extension Based on Fuzzy Relation

IEEE TRANSACTIONS ON NANOBIOSCIENCE

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IEEE TRANSACTIONS ON NANOBIOSCIENCE 2016年第7期15卷 728-738页

作者： cao, buwen Luo, Jiawei Liang, Cheng Wang, Shulin Ding, Pingjian Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Hunan City Univ Sch Informat Sci & Engn Yiyang 413000 Peoples R China Shandong Normal Univ Sch Informat Sci & Engn Jinan 250014 Peoples R China

Identifying overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complexes detection have been developed for PPI networks. However, majority of algorithms primarily depend on network topological feature and/or gene expression profile, failing to consider the inherent biological meanings between protein pairs. In this paper, we propose a novel method to detect protein complexes using pseudo-clique extension based on fuzzy relation (PCE-FR). Our algorithm operates in three stages: it first forms the nonoverlapping protein substructure based on fuzzy relation and then expands each substructure by adding neighbor proteins to maximize the cohesive score. Finally, highly overlapped candidate protein complexes are merged to form the final protein complex set. Particularly, our algorithm employs the biological significance hidden in protein pairs to construct edge weight for protein interaction networks. The experiment results show that our method can not only outperform classical algorithms such as CFinder, ClusterONE, CMC, RRW, HC-PIN, and ProRank +, but also achieve ideal overall performance in most of the yeast PPI datasets in terms of composite score consisting of precision, accuracy, and separation. We further apply our method to a human PPI network from the HPRD dataset and demonstrate it is very effective in detecting protein complexes compared to other algorithms.

关键词： PPI Protein complex Pseudo-clique extension Fuzzy relation

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A cell-core-attachment approach for identifying protein complexes in yeast protein-protein interaction network 11

A cell-core-attachment approach for identifying protein comp...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Luo, Jiawei Lin, Dingyu cao, buwen Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Hunan Univ Collaborat & Innovat Ctr Digital Chinese Med Proj Changsha 410082 Hunan Peoples R China

With the increasing of available protein-protein interaction (PPI) data, many computational methods have been explored to identify protein complexes from PPI networks. Majority of algorithms employ the feature of local neighbors to detect local dense subgraphs which correspond to protein complexes. Those approaches neglect the inherent core-attachment structure of protein complexes, which to an extent affect the protein complexes of prediction accuracy. In this paper, we propose a new algorithm for predicting protein complexes, deriving from the framework of the core-attachment. The proposed method first obtains the triangular structures of the core of protein complexes, name as cells, in which the edge-clustering coefficient is used. And then the cells are expanded to protein complex cores based on the closeness. Finally, the attachments are added to their corresponding cores to form the final protein complexes. The experimental results on two yeast PPI data show our method outperform the existing algorithms in terms of matched protein complexes and biological significance using two benchmark data sets.

关键词： Core-attachment protein complex protein-protein interaction triangular structure

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Identifying miRNA-mRNA Regulatory Modules Based on Overlapping Neighborhood Expansion from Multiple Types of Genomic Data 12th

Identifying miRNA-mRNA Regulatory Modules Based on Overlappi...

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12th International Conference on Intelligent Computing (ICIC)

作者： Luo, Jiawei Liu, Bin cao, buwen Wang, Shulin Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Hunan Univ Collaborat & Innovat Ctr Digital Chinese Med 2011 Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9783319422916;9783319422909

MicroRNA (miRNA)-mRNA regulatory modules are key entities to disorders. Several computational methods are developed to identify miRNA-mRNA modules. Although these methods have achieved ideal performance, the number of modules needed to be predefined. Therefore, identification of modules is still computationally challenging. In this study, a new algorithm called MiRMD (miRNA-mRNA Regulatory Modules Detection) is presented to identify miRNA-mRNA modules, which do not need to predefine the number of modules. Firstly, a miRNA-mRNA regulatory network is constructed, then core structures are detected in this network by merging cohesive modules. Next, some overlapping neighbor nodes are added into the cores according to the density. Finally, some overlap modules are filtered. The experimental results based on three cancers datasets show that modules identified by MiRMD are more coherent and functional enriched than the other two methods according to MiMEC and GO enrichment. Particularly, modules identified by our method are strongly implicated in cancer.

关键词： miRNA-mRNA regulatory modules Cores Merging Density

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