检索结果-内蒙古大学图书馆

19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Pang, Jun Qin, Hong-Chao Liu, Yan Liu, Xiao-Qi Wuhan University of Science and Technology Hubei430065 China Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System Hubei430065 China Beijing Institute of Technology Beijing100081 China Wuhan Jiangang Middle School Hubei430050 China

ISBN: (纸本)9783031466632

Knowledge hypergraph link prediction aims to predict missing relationships in knowledge hypergraphs and is one of the effective methods for graph completion. The existing optimal knowledge hypergraph link method based on tensor decomposition, i.e., GETD (Generalized Model based on Tucker Decomposition and Tensor Ring Decomposition), has achieved good performance by extending Tucker decomposition, but there are still two main problems: (1)GETD does not establish operations or connections between any two tensor factors, resulting in limited representation of tensor correlation (referred to as finiteness);(2)The tensor decomposed by GETD is highly sensitive to the arrangement of tensor patterns (referred to as sensitivity). In response to the above issues, we propose a knowledge hypergraph link prediction model, called GETD+, based on fully-connected tensor decomposition(FCTN). By combining Tucker decomposition and FCTN, a multi-linear operation/connection is established for any two factor tensors obtained from tensor decomposition. This not only enhances the representation ability of tensors, but also eliminates sensitivity to tensor pattern arrangement. Finally, the superiority of the GETD+ model was verified through a large number of experiments on real knowledge hypergraph datasets and knowledge graph datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tensors

来源：评论

学校读者我要写书评

暂无评论

Prediction of Drug-Target Interactions Using Molecular Graph and GDNet-DTI Model

Prediction of Drug-Target Interactions Using Molecular Graph...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Xu, Shuai Lin, Xiaoli Yu, Haiping Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Inf Ormation Processing and Realtime Industrial System School of Computer Science Huanggang Normal University Wuhan China

ISBN: (纸本)9781665401265

The prediction of drug-target interactions (DTIs) is of great significance to the fields of drug design and drug development. However, traditional biological experiments are time-consuming and cost-effective, which has prompted more people to turn their attention to the use of computers to assist in predicting DTIs. This paper proposes an improved prediction model based on multiple graph representation methods, which is GDNet-DTI that combined GCN and DeepWalk. First, a molecular map with atoms as nodes and chemical bonds as edges is generated using the SMILE sequence of drugs, and then GIN is used to extract the features of molecular map for better obtaining the complex interactions between atoms. For target proteins, the protein sequence is first represented by a word vector, and then the one-dimensional convolution is used to extract features for extracting the different levels of features. Then, based on obtained drug features and target features, a DTI-graph is generated, in which drugs and targets are represented as nodes and interactions are represented as edges. Finally, GDNet-DTI are used to obtain node neighborhood information and graph topology information of the DTI-graph. Compared with other advanced models, the results show that GDNet-DTI combined with multiple graph features can predict DTIs more accurately and effectively with DrugBank and four benchmark datasets. In addition, a case study with COVID-19 data is presented, which shows that the proposed method has the potential to predict the actual DTIs and can contribute to the development of drug discovery. © 2021 IEEE.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Drug-Target Interaction Prediction Based on Drug Subgraph Fingerprint Extraction Strategy and Subgraph Attention Mechanism 9th

Drug-Target Interaction Prediction Based on Drug Subgraph Fi...

引用

19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Wang, Lizhi Zhang, Xiaolong Lin, Xiaoli Hu, Jing College of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System Hubei Wuhan China Institute of Big Data Science and Engineering Wuhan University of Science and Technology Hubei Wuhan China

ISBN: (纸本)9783031466700

Drug discovery is a major focus of modern research, and predicting drug-target interactions is one of the strategies to facilitate this research process. Traditional laboratory methods have long time cycles and are relatively costly, so the use of high-precision virtual screening is essential. Previous virtual screens have only considered the sequence structure of the drug or the graph structure of the drug, without considering the role of the drug subgraph structure. Therefore, this paper adopts a method for extracting drug subgraphs and designs a strategy for extracting drug subgraph fingerprints. Then, the fingerprint vector data of the drugs are trained by the graph neural network to produce drug information vectors containing the spatial structure of the drug subgraph. Finally, the subgraph attention mechanism is used to update the information on protein targets, facilitating the model to extract more information about the target site. The experimental results show that the model can predict drug-target interactions well, outperforming the state-of-the-art model in all metrics. And it is also effective in both classification prediction and affinity regression prediction. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Prediction of Drug-Target Interactions Using Molecular Graph and GDNet-DTI Model

Prediction of Drug-Target Interactions Using Molecular Graph...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Shuai Xu Xiaoli Lin Haiping Yu Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Inf ormation Processing and Realtime Industrial System School of Computer Science Huanggang Normal University Wuhan China

ISBN: (纸本)9781665429825

关键词： Drugs COVID-19 Biological system modeling Predictive models Feature extraction Prediction algorithms Data models

来源：评论

学校读者我要写书评

暂无评论

DLIENet: A lightweight low-light image enhancement network via knowledge distillation

引用

Pattern Recognition 2026年 169卷

作者： Ling Zhang Zhenyu Li Liang Cheng Qing Zhang Zheng Liu Xiaolong Zhang Chunxia Xiao School of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430081 China Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System Wuhan University of Science and Technology Wuhan 430081 China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou 510275 China School of Computer Science China University of Geosciences (Wuhan) Wuhan 430074 China School of Computer Science Wuhan University Wuhan 430072 China

Learning-based methods have recently made remarkable progress for low-light image enhancement (LIE). However, most of these methods generally suffer from unsatisfactory results with overexposure and noise residual. To address these issues, we propose a distillation-based low-light image enhancement network (DLIENet), where we construct a teacher network for LIE to help train DLIENet with lightweight architecture. DLIENet imitates the low-light enhancement capability of the teacher network through knowledge distillation. Concretely, the distillation in the bottleneck module transfers the feature representation capability from the pre-trained teacher network to DLIENet. The distillation in content attention module forces DLIENet to focus on the key contents for reconstructing normal-light images, promoting noise-free results for the low-light images. Additionally, we employ a two-step joint training strategy to improve the performance of the teacher network for LIE. Moreover, we introduce a feature fusion block in DLIENet to normalize the features globally, avoiding non-uniform illumination and overexposure. Extensive experiments and evaluations on both the synthetic and real-world datasets demonstrate the generalization and generalization ability of our method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Evidence-based treatment of medical guideline

Communications in Computer and Information Science

引用

Communications in Computer and information Science 2014年 480卷 185-197页

作者： Tian, Pingfang Zhu, Zhonghua Huang, Zhisheng College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Realtime Industrial System Wuhan430065 China Department of Computer Science Vrije University of Amsterdam AmsterdamHV1081 Netherlands

Medical guidelines are recommendations on the appropriate treatment and care of people with specific diseases and conditions. Evidence-based medical guidelines are the document or recommendations which have been annotated with their relevant medical evidences, namely research findings from medical publications. We have observed the fact that there exist significant amount of medical guidelines have not yet annotated with relevant medical evidences, which becomes even more serious in the Chinese medical guidelines. In this paper, we propose an approach of evidence process of medical guidelines, such that we can find relevant evidences for those non-evidence-based medical guidelines. We develop a system called Link2Pubmed, which can retrieve the text which is described with a natural language and get the corresponding medical evidences. We use the word segmentation and part-of-speech tagging tools in natural language processing (NLP) to extract the keywords, and then translate them into corresponding English concepts in SNOMED CT, a well-known medical ontology. This system is an attempt to solve the existing problems in Chinese medical guidelines, which lack the annotations of relevant evidences. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Natural language processing systems

来源：评论

学校读者我要写书评

暂无评论

Pharmaceutical semantic database query mechanism based on keywords

Pharmaceutical semantic database query mechanism based on ke...

引用

7th Chinese Semantic Web Symposium and the 2nd Chinese Web ScienceConference, CSWS 2013

作者： Sun, Juan Gu, Jinguang Huang, Zhisheng Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Realtime Industrial System Wuhan 430065 China Department of Computer Science Vrije University of Amsterdam 1081hv Amsterdam Netherlands

ISBN: (纸本)9783642540240

With the development of the Internet, Semantic search technology has been applied to various fields. In the pharmaceutical field, because of the huge number and wide range of the drugs on the market, an effective semantic retrieval system can bring great convenience for doctors. However, for doctors who have no background knowledge of semantic technologies, to write formal semantics search statement is still very difficult. Therefore, this paper proposes a pharmaceutical semantic database query mechanism which based on keywords. In this query mechanism, by analysis of ontology database and establishment of Chinese - English translation mapping, we can Transform Chinese keywords into English keywords. Then match the English keywords with ontology database to build a list of triples. And finally, formed Corresponding SPARQL query by analyze the list. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Database systems

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：