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Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph-Based Approach

JOURNAL OF MEDICAL INTERNET RESEARCH 2023年第1期25卷 e45225页

作者： Lou, Pei Fang, An Zhao, Wanqing Yao, Kuanda Yang, Yusheng Hu, Jiahui Chinese Acad Med Sci & Peking Union Med Coll Inst Med Informat 3 Yabao Rd Beijing 100020 Peoples R China

Background: The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. Objective: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach. Methods: We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. Results: The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33

关键词： coronavirus heterogeneous data integration knowledge graph embedding drug repurposing interpretable prediction COVID-19

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Designing a data Exchange Model for the Directorate of Special Programs Systems - Namibia

Designing a Data Exchange Model for the Directorate of Speci...

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IST-Africa Conference (IST-Africa)

作者： Kandume, Puyeipawa Hamunyel, Suama Namibia Univ Sci & Technol Jackson Kaujeua St Windhoek 9000 Namibia

ISBN: (纸本)9781905824670

In many developing countries, Health Information Systems (HIS) interoperability is a major challenge [1]. In Namibia, the Directorate of Special Programs has several times attempted to integrate the existing HIS, despite continuous support made by numerous donors [2] these efforts were futile. This paper proposed a data exchange model that will enable the Electronic Patient Management System (EPMS) and the Electronic Dispensing Tool (EDT) to interoperate without redesigning their current architectural designs. The data exchange model aims to address the issues of incomplete patient records, access to real-time patient data, and discrepancies in reports from the EPMS and the EDT. The data sources included user manuals, data dictionaries, and semi-structured interviews with relevant technical personnel that administer the systems. The two systems have a wide range of common input data fields, which if shared can be a solution to incomplete records and reporting discrepancies. A bi-directional data exchange model is recommended for the two systems to communicate.

关键词： HIS interoperability heterogeneous data integration data exchange model

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Big data architecture for intelligent maintenance: a focus on query processing and machine learning algorithms

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JOURNAL OF BIG data 2020年第1期7卷 1-26页

作者： Lehmann, Claude Huber, Lilach Goren Horisberger, Thomas Scheiba, Georg Sima, Ana Claudia Stockinger, Kurt Zurich Univ Appl Sci Obere Kirchgasse 2 CH-8400 Winterthur Switzerland Bystron Laser AG Ind Str 21 CH-3362 Niederonz Switzerland

Exploiting available condition monitoring data of industrial machines for intelligent maintenance purposes has been attracting attention in various application fields. Machine learning algorithms for fault detection, diagnosis and prognosis are popular and easily accessible. However, our experience in working at the intersection of academia and industry showed that the major challenges of building an end-to-end system in a real-world industrial setting go beyond the design of machine learning algorithms. One of the major challenges is the design of an end-to-end data management solution that is able to efficiently store and process large amounts of heterogeneous data streams resulting from a variety of physical machines. In this paper we present the design of an end-to-end Big data architecture that enables intelligent maintenance in a real-world industrial setting. In particular, we will discuss various physical design choices for optimizing high-dimensional queries, such as partitioning and Z-ordering, that serve as the basis for health analytics. Finally, we describe a concrete fault detection use case with two different health monitoring algorithms based on machine learning and classical statistics and discuss their advantages and disadvantages. The paper covers some of the most important aspects of the practical implementation of such an end-to-end solution and demonstrates the challenges and their mitigation for the specific application of laser cutting machines.

关键词： Prognostics and health management Intelligent maintenance Big data architecture heterogeneous data integration Stream processing Query processing Machine learning

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A Cooperative Multi-Agent System for Crowd Sensing Based Estimation in Smart Cities

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IEEE ACCESS 2020年 8卷 183051-183070页

作者： Guastella, Davide Andrea Camps, Valerie Gleizes, Marie-Pierre Univ Toulouse III Paul Sabatier Inst Rech Informat Toulouse F-31330 Toulouse France Univ Palermo Dipartimento Matemat & Informat I-90133 Palermo Italy

The concept of Smart City has spread as a solution to ensure better access to information and services to citizens, but also as a means to reduce the environmental footprint of cities. To this end, a continuous and wide observation of the environment is necessary to analyze information that enables government bodies to act on the environment appropriately. Moreover, a diffused acquisition of information requires adequate infrastructure and proper devices, which results in relevant installation and maintenance costs. Our proposal enables reducing the number of necessary sensors to be deployed while ensuring that information is available at any time and anywhere. We present the HybridIoT system to cope with the lack of environmental information in the urban context through an estimation technique that integrates heterogeneous data acquired from some different sensors. HybridIoT can be deployed in large-scale contexts and ensures data accessibility even if devices enter or leave the system at any time and everywhere. We compare the results to those obtained by state-of-the-art techniques to assess the validity of our proposal, in particular concerning the properties of openness, large-scale, and heterogeneity, of primary importance in the context of the development of systems to be deployed in the smart city context.

关键词： Estimation Smart cities Intelligent sensors data integration Support vector machines Smart city cooperative multi-agent systems missing information estimation heterogeneous data integration

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An optimized data integration model based on reverse cleaning for heterogeneous multi-media data

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MULTIMEDIA TOOLS AND APPLICATIONS 2016年第23期75卷 15571-15586页

作者： Chen, Hao Ouyang, Yueqi Jiang, Wen Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Hunan Vocat Coll Sci & Technol Sch Software Changsha 410118 Hunan Peoples R China

With the continuous development of information technology, various multi-media data are constantly emerging and presents the characteristics of autonomous and heterogeneous, how to integrate and analysis data more correctly and efficiently has become a challenging problem. Firstly, in order to improve the quality of the integrated data, two real-time threads combined with data adapter are used to monitor and refresh necessary updates from heterogeneous data efficiently. Once the original data has been updated, the real-time data will be loaded into the data center soon. Secondly, a data reverse cleaning method is proposed to improve the data quality. It uses the data source tree that built in the data integration process to find the location of the original data quickly after reverse cleaning. finally, a data accuracy assessment algorithm is designed for data quality assessment, which is based on Bayesian network and the path condition algorithm. Experimental results show that the quality of the integrated data significantly higher than the quality of the original data.

关键词： Multi-media data heterogeneous data integration Reverse cleaning

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data service generation framework from heterogeneous printed forms using semantic link discovery

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年第Part2期79卷 514-527页

作者： Yu, Han Cai, Hongming Zhou, Jun Jiang, Lihong Shanghai Jiao Tong Univ Sch Software 800 Dongchuan Rd Shanghai Peoples R China

Printed forms contain rich information in business process and daily life. However, tremendous heterogeneous printed forms containing same categories of information are difficult to manage and share, which lead to massive data in printed forms remaining waste. To automatically integrate and share these data remarkably improves the efficiency of enterprises, the key problem is how to extract heterogeneous data in printed forms and integrate them for quick use. To solve this issue, we propose a framework that discovers semantic links in printed forms and generates data services for easy data management and rapid data sharing in the enterprise systems. First, a multiple-OCR-based form recognition approach is proposed to make forms computer-readable. Next, forms are modeled into semi-structured data using structure based semantic link discovery and refining with massive data. Then, a linked data model is built by table matching to align data. Finally, data services are generated based on the linked data model. A series of experiments on printed resumes are conducted, and the results illustrate our framework performs well in recognition rate, link discovery accuracy, data compression ratio and data resource accuracy. A prototype system is presented to illustrate the feasibility of the proposed framework. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Form recognition heterogeneous data integration Semantic data model Table matching data service generation

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Learning important features from multi-view data to predict drug side effects

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JOURNAL OF CHEMINFORMATICS 2019年第1期11卷 79-79页

作者： Liang, Xujun Zhang, Pengfei Li, Jun Fu, Ying Qu, Lingzhi Chen, Yongheng Chen, Zhuchu Cent South Univ Xiangya Hosp NHC Key Lab Canc Prote XiangYa Rd Changsha Peoples R China

The problem of drug side effects is one of the most crucial issues in pharmacological development. As there are many limitations in current experimental and clinical methods for detecting side effects, a lot of computational algorithms have been developed to predict side effects with different types of drug information. However, there is still a lack of methods which could integrate heterogeneous data to predict side effects and select important features at the same time. Here, we propose a novel computational framework based on multi-view and multi-label learning for side effect prediction. Four different types of drug features are collected and graph model is constructed from each feature profile. After that, all the single view graphs are combined to regularize the linear regression functions which describe the relationships between drug features and side effect labels. L1 penalties are imposed on the regression coefficient matrices in order to select features relevant to side effects. Additionally, the correlations between side effect labels are also incorporated into the model by graph Laplacian regularization. The experimental results show that the proposed method could not only provide more accurate prediction for side effects but also select drug features related to side effects from heterogeneous data. Some case studies are also supplied to illustrate the utility of our method for prediction of drug side effects.

关键词： Side effect prediction heterogeneous data integration Feature selection

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GNE: a deep learning framework for gene network inference by aggregating biological information

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BMC SYSTEMS BIOLOGY 2019年第Sup2期13卷 38-38页

作者： Kishan, K. C. Li, Rui Cui, Feng Yu, Qi Haake, Anne R. Rochester Inst Technol Golisano Coll Comp & Informat Sci 20 Lomb Mem Dr Rochester NY 14623 USA Rochester Inst Technol Thomas H Gosnell Sch Life Sci 84 Lomb Mem Dr Rochester NY 14623 USA

BackgroundThe topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed *** propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low- dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database *** proposed model demonstrates the importance of integrating heterogeneous information about genes for gene network inference. GNE is freely available under the GNU General Public License and can be downloaded from GitHub (https://***/kckishan/GNE).

关键词： Gene interaction networks Gene expression Network embedding heterogeneous data integration Deep learning

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Integrated Semantic Model for Complex Disease Network 8th

Integrated Semantic Model for Complex Disease Network

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8th Joint International Semantic Technology Conference (JIST)

作者： Park, Junho Yang, Sungkwon Kim, Hong-Gee Seoul Natl Univ Biomed Knowledge Engn Lab Seoul South Korea

ISBN: (纸本)9783030042844;9783030042837

To understand biological phenomena, biologists have identified the interactions between biological molecules in vivo. Until recently, all of the unique and interactive information of such molecules has been built into a database and made available online. Among them, there was an effort to understand the relationship of molecules based on biological pathways, and a standard model called BioPAX was made to enable interchange and operation of data. In particular, Pathway Commons integrates other biological data besides biological pathways using BioPAX. We are interested in identifying the molecular mechanisms of disease and recommending drugs for treatment. In addition to data provided by Pathway Commons, additional disease and drug data was added to be used in various analysis. We extended the model to express the data that BioPAX could not cover and converted all the data to RDF based on the model. We integrate and present diverse biological data using semantic technologies from the perspective of representing disease networks. We hope that this information will aid in a deeper understanding of disease and drug recommendations.

关键词： Schema modelling Knowledge representation heterogeneous data integration

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Online Application of Science and Technology Program Oriented Distributed heterogeneous data integration

Online Application of Science and Technology Program Oriente...

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2011 3rd International Conference on Computer Research and Development(ICCRD 2011)

作者： TANG Hao CHEN Hao LIANG Ying SHI Hongzhou Institute of Computing Technology Chinese Academy of Sciences Graduate University of Chinese Academy of Sciences

ISBN: (纸本)9781612848372

This paper proposes a heterogeneous data integration method based on object mapping to solve the problem of mass of heterogeneous data existing in online application of science and technology programs. Based on specific data standards, this method organizes data into tree models through aggregation of logic, describes the file structures of different science and technology programs through redefining the pruning of the models, and takes the path of a data item in the model as the sole mark of the data item, thus screening the heterogeneity of data. It lowers the cost for maintenance and management of scientific and technological data and realizes data integration and unified management. This method finds applications in the actual integrative managing environment of science and technology programs.

关键词： heterogeneous data integration meta-data data template data conflict XML

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