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iMDA-BN: Identification of miRNA-disease associations based on the biological network and graph embedding algorithm

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL 2020年 18卷 2391-2400页

作者： Zheng, Kai You, Zhu-Hong Wang, Lei Guo, Zhen-Hao China Univ Min & Technol Sch Comp Sci & Technol Xuzhou 221116 Jiangsu Peoples R China Chinese Acad Sci Xinjiang Tech Inst Phys & Chem Urumqi 830011 Peoples R China Zaozhuang Univ Coll Informat Sci & Engn Zaozhuang 277100 Peoples R China

Benefiting from advances in high-throughput experimental techniques, important regulatory roles of miRNAs, lncRNAs, and proteins, as well as biological property information, are gradually being complemented. As the key data support to promote biomedical research, domain knowledge such as intermolecular relationships that are increasingly revealed by molecular genome-wide analysis is often used to guide the discovery of potential associations. However, the method of performing network representation learning from the perspective of the global biological network is scarce. These methods cover a very limited type of molecular associations and are therefore not suitable for more comprehensive analysis of molecular network representation information. In this study, we propose a computational model based on the Biological network for predicting potential associations between miRNAs and diseases called iMDA-BN. The iMDA-BN has three significant advantages: I) It uses a new method to describe disease and miRNA characteristics which analyzes node representation information for disease and miRNA from the perspective of biological networks. II) It can predict unproven associations even if miRNAs and diseases do not appear in the biological network. III) Accurate description of miRNA characteristics from biological properties based on high-throughput sequence information. The iMDA-BN predictor achieves an AUC of 0.9145 and an accuracy of 84.49% on the miRNA-disease association baseline dataset, and it can also achieve an AUC of 0.8765 and an accuracy of 80.96% when predicting unknown diseases and miRNAs in the biological network. Compared to existing miRNA-disease association prediction methods, iMDA-BN has higher accuracy and the advantage of predicting unknown associations. In addition, 45, 49, and 49 of the top 50 miRNA-disease associations with the highest predicted scores were confirmed in the case studies, respectively. (C) 2020 The Authors. Published by Elsevier B.V. on

关键词： miRNA Disease Heterogenous information The biological network graph embedding algorithm

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A time series classification method combining graph embedding and the bag-of-patterns algorithm

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APPLIED INTELLIGENCE 2023年第22期53卷 26297-26312页

作者： Ma, Xiaoxuan Yu, Mengping Huang, Huan Hou, Rui Dong, Mianxiong Ota, Kaoru Zeng, Deze South Cent Minzu Univ Coll Comp Sci Wuhan Hubei Peoples R China South Cent Minzu Univ Sch Educ Wuhan Hubei Peoples R China Muroran Inst Technol Dept Informat & Elect Engn Muroran Hokkaido Japan China Univ Geosci Sch Comp Sci Wuhan Hubei Peoples R China

Time series data mining techniques have attracted extensive attention from researchers worldwide. Of these techniques, time series classification is an important part of time series mining. Among the many time series classification algorithms, methods based on the bag-of-patterns algorithm have attracted much attention from researchers because of their high accuracy and execution efficiency. However, when using these methods, only the frequency of different patterns is considered. Features such as the position of patterns in a sequence are not mined. Therefore, the aim of this paper is to determine how to solve the problem that the positional relationships among patterns are ignored when using the bag-of-patterns algorithm. To solve this issue, we introduce the graph embedding technique, and an attempt is made to capture the positional relationships among the patterns of time series from the graph perspective. To verify the performance of the method, we perform extensive experiments with the UCR time series archive, and the experimental results demonstrate that our proposed method generally improves the classification ability of models based on the bag-of-patterns algorithm.

关键词： Time series classification graph embedding algorithm Bag-of-patterns algorithm

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Data-Driven Predictive Modeling of Citywide Crowd Flow for Urban Safety Management: A Case Study of Beijing, China

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JOURNAL OF FORECASTING 2025年第2期44卷 730-752页

作者： Jiang, He Zhang, Xuxilu Dong, Yao Wang, Jianzhou Xi An Jiao Tong Univ Sch Econ & Finance Xian Peoples R China UPC BarcelonaTech BIT Barcelona Innovat Transportat Barcelona Spain Xian Univ Finance & Econ Sch Stat Xian Peoples R China Macau Univ Sci & Technol Inst Syst Engn Macau Peoples R China

Crowd flow forecasting is vital for urban planning, resource allocation, and public safety, particularly in the context of the COVID-19 pandemic. However, traditional predictive models struggle to capture the complex, nonlinear spatial-temporal relationships inherent in crowd flow data due to its irregular volatility. To address these limitations, this paper proposes the innovative citywide crowd flow prediction (CCFP) model, which merges statistical rules with machine learning techniques (XGBoost, LightGBM, and CatBoost). The CCFP model is specifically designed to leverage spatial dependencies and two-level periodicity (weekly and daily) in population flow to predict crowd flow indexes (CFI$$ CFI $$) within specific areas. We employ an urban area graph created using the Node2Vec algorithm to capture the temporal and spatial nuances of human flow patterns. Notably, this study innovatively incorporates migration, weather, and epidemic data into machine-learning models for feature extraction. Moreover, it introduces weighted factors-growth,base,week$$ growth, base, week $$, and day$$ day $$-to enhance the accuracy of CFI$$ CFI $$ prediction. Among the combined models, CCFP outperforms others with remarkable scientific precision (root mean squared error, RMSE=2.04$$ RMSE=2.04 $$;mean absolute error, MAE=0.81$$ MAE=0.81 $$;mean absolute percentage error, MAPE=0.13$$ MAPE=0.13 $$). Overall, the CCFP model represents a significant advancement in crowd flow prediction, offering valuable insights for urban safety management and city planning during pandemics.

关键词： citywide crowd flow prediction graph embedding algorithm machine learning spatio-temporal data mining urban safety management

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HEGANLDA: A Computational Model for Predicting Potential Lncrna-Disease Associations Based On Multiple Heterogeneous Networks

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第1期20卷 388-398页

作者： Li, Jianwei Wang, Duanyang Yang, Zhenwu Liu, Ming Hebei Univ Technol Tianjin 300401 Peoples R China

Long non-coding RNAs (lncRNAs) play vital regulatory roles in many human complex diseases, however, the number of validated lncRNA-disease associations is notable rare so far. How to predict potential lncRNA-disease associations precisely through computational methods remains challenging. In this study, we proposed a novel method, LDVCHN (LncRNA-Disease Vector Calculation Heterogeneous Networks), and also developed the corresponding model, HEGANLDA (Heterogeneous embedding Generative Adversarial Networks LncRNA-Disease Association), for predicting potential lncRNA-disease associations. In HEGANLDA, the graph embedding algorithm (HeGAN) was introduced for mapping all nodes in the lncRNA-miRNA-disease heterogeneous network into the low-dimensional vectors which severed as the inputs of LDVCHN. HEGANLDA effectively adopted the XGBoost (eXtreme Gradient Boosting) classifier, which was trained by the low-dimensional vectors, to predict potential lncRNA-disease associations. The 10-fold cross-validation method was utilized to evaluate the performance of our model, our model finally achieved an area under the ROC curve of 0.983. According to the experiment results, HEGANLDA outperformed any one of five current state-of-the-art methods. To further evaluate the effectiveness of HEGANLDA in predicting potential lncRNA-disease associations, both case studies and robustness tests were performed and the results confirmed its effectiveness and robustness. The source code and data of HEGANLDA are available at https://***/HEGANLDA/HEGANLDA.

关键词： graph embedding algorithm heterogeneous network lncRNA-disease association prediction XGBoost classifier

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Multiple Object Tracking through Background Learning

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Computer Systems Science & Engineering 2023年第1期44卷 191-204页

作者： Deependra Sharma Zainul Abdin Jaffery Jamia Millia Islamia Department of Electrical EngineeringNew-Delhi110025India

This paper discusses about the new approach of multiple object track-ing relative to background *** concept of multiple object tracking through background learning is based upon the theory of relativity,that involves a frame of reference in spatial domain to localize and/or track any *** of multiple object tracking has seen a lot of research,but researchers have considered the background as ***,in object tracking,the back-ground plays a vital role and leads to definite improvement in the overall process of *** the present work an algorithm is proposed for the multiple object tracking through background *** learning framework is based on graph embedding approach for localizing multiple *** graph utilizes the inher-ent capabilities of depth modelling that assist in prior to track occlusion avoidance among multiple *** proposed algorithm has been compared with the recent work available in literature on numerous performance evaluation *** is observed that our proposed algorithm gives better performance.

关键词： Object tracking image processing background learning graph embedding algorithm computer vision

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Internet Financial Fraud Detection Based on a Distributed Big Data Approach With Node2vec

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IEEE ACCESS 2021年 9卷 43378-43386页

作者： Zhou, Hangjun Sun, Guang Fu, Sha Wang, Linli Hu, Juan Gao, Ying Hunan Univ Finance & Econ Dept Informat Technol & Management Changsha 410205 Peoples R China Univ Alabama Coll Engn Tuscaloosa AL 35401 USA

The rapid development of information technologies like Internet of Things, Big Data, Artificial Intelligence, Blockchain, etc., has profoundly affected people's consumption behaviors and changed the development model of the financial industry. The financial services on Internet and IoT with new technologies has provided convenience and efficiency for consumers, but new hidden fraud risks are generated also. Fraud, arbitrage, vicious collection, etc., have caused bad effects and huge losses to the development of finance on Internet and IoT. However, as the scale of financial data continues to increase dramatically, it is more and more difficult for existing rule-based expert systems and traditional machine learning model systems to detect financial frauds from large-scale historical data. In the meantime, as the degree of specialization of financial fraud continues to increase, fraudsters can evade fraud detection by frequently changing their fraud methods. In this article, an intelligent and distributed Big Data approach for Internet financial fraud detections is proposed to implement graph embedding algorithm Node2Vec to learn and represent the topological features in the financial network graph into low-dimensional dense vectors, so as to intelligently and efficiently classify and predict the data samples of the large-scale dataset with the deep neural network. The approach is distributedly performed on the clusters of Apache Spark graphX and Hadoop to process the large dataset in parallel. The groups of experimental results demonstrate that the proposed approach can improve the efficiency of Internet financial fraud detections with better precision rate, recall rate, F1-Score and F2-Score.

关键词： Credit cards Finance Data models Internet of Things Expert systems Clustering algorithms Big Data Internet of Things (IoT) Internet finance fraud detection graph embedding algorithm Node2Vec

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Particle Swarm algorithm for Smart Contract Vulnerability Detection Based on Semantic Web

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INTERNATIONAL JOURNAL ON SEMANTIC WEB AND INFORMATION SYSTEMS 2024年第1期20卷 1页

作者： Feng, Tao Cui, Yuyang Lanzhou Univ Technol Sch Comp & Commun Lanzhou Peoples R China

In recent years, smart contracts have risen rapidly in the blockchain field, but security issues have also become increasingly prominent. Due to the lack of unified evaluation standards, the security analysis of smart contracts mainly relies on complex and not easily scalable expert rules. To address these issues, we employ slicing techniques to reduce the interference of extraneous code on the detection process, apply normalisation techniques to eliminate the differences between different compiler versions and use particle swarm optimisation algorithms to determine the similarity between contracts, thus improving the accuracy and efficiency of detection. In addition, we combine a variety of features such as static analysis, dynamic analysis and symbolic execution to gain a more comprehensive understanding of contract characteristics and behaviours for more accurate vulnerability identification. Experimental results show that the scheme significantly improves the detection capability and provides a new solution for the security detection of smart contracts.

关键词： graph embedding algorithm Multimodal Feature Fusion Particle Swarm Optimisation algorithm Smart Contracts Vulnerability Detection

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