检索结果-内蒙古大学图书馆

A unified GCNN model for predicting CYP450 inhibitors by using graph convolutional neural networks with attention mechanism

引用

COMPUTERS IN BIOLOGY AND MEDICINE 2022年 150卷 106177-106177页

作者： Qiu, Minyao Liang, Xiaoqi Deng, Siyao Li, Yufang Ke, Yanlan Wang, Pingqing Mei, Hu Chongqing Univ Coll Bioengn Key Lab Biorheol Sci & Technol Minist Educ Chongqing 400044 Peoples R China Chongqing Univ Coll Bioengn Chongqing 400044 Peoples R China Minist Educ Key Lab Biorheol Sci & Technol Chongqing 400044 Peoples R China

Undesirable drug-drug interactions (DDIs) may lead to serious adverse side effects when more than two drugs are administered to a patient simultaneously. One of the most common DDIs is caused by unexpected inhibition of a specific human cytochrome P450 (CYP450), which plays a dominant role in the metabolism of the co -administered drugs. Therefore, a unified and reliable method for predicting the potential inhibitors of CYP450 family is extremely important in drug development. In this work, graph convolutional neural network (GCN) with attention mechanism and 1-D convolutional neural network (CNN) were used to extract the features of CYP ligands and the binding sites of CYP450 respectively, which were then combined to establish a unified GCN-CNN (GCNN) model for predicting the inhibitors of 5 dominant CYP isoforms, i.e., 1A2, 2C9, 2C19, 2D6, and 3A4. Overall, the established GCNN model showed good performances on the test samples and achieved better per-formances than the recently proposed iCYP-MFE model by using the same datasets. Based on the heat-map analysis of the resulting molecular graphs, the key structural determinants of the CYP inhibitors were further explored.

关键词： Cytochrome P450 Drug-drug interactions graph convolutional neural network Attention mechanism Inhibitor

来源：评论

学校读者我要写书评

暂无评论

Action Recognition Using Attention-Joints graph convolutional neural networks

引用

IEEE ACCESS 2020年 8卷 305-313页

作者： Ahmad, Tasweer Mao, Huiyun Lin, Luojun Tang, Guozhi South China Univ Technol Sch Elect & Informat Engn Guangzhou 510000 Peoples R China COMSATS Univ Islamabad Dept Elect Engn Sahiwal Campus Sahiwal 57000 Pakistan South China Univ Technol Sch Comp Sci Guangzhou 510000 Peoples R China

Human skeleton contains significant information about actions, therefore, it is quite intuitive to incorporate skeletons in human action recognition. Human skeleton resembles to a graph where body joints and bones mimic to graph nodes and edges. This resemblance of human skeleton to graph structure is the main motivation to apply graph convolutional neural network for human action recognition. Results show that the discriminant contribution of different joints is not equal for different actions. Therefore, we propose to use attention-joints that correspond to joints significantly contributing to the specific actions. Features corresponding to only these attention-joints are computed and assigned as node features of the graph. In our method, node features (also termed as attention-joint features) include the i) distances of attention-joints from the center-of-gravity of human body, ii) distances between adjacent attention-joints and iii) joints flow features. The proposed method gives a simple but more efficient representation of skeleton sequences by concatenating more relative distances and relative coordinates to other joints. The proposed methodology has been evaluated on single image Stanford 40-Actions dataset, as well as on temporal skeleton-based action recognition PKU-MDD and NTU-RGBD datasets. Results show that this framework outperforms existing state-of-the-art methods.

关键词： Human action recognition attention-joints graph convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Human-airway surface mesh smoothing based on graph convolutional neural networks

引用

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024年 246卷 108061-108061页

作者： Ho, Thao Thi Tran, Minh Tam Cui, Xinguang Lin, Ching -Long Baek, Stephen Kim, Woo Jin Lee, Chang Hyun Jin, Gong Yong Chae, Kum Ju Choi, Sanghun Kyungpook Natl Univ Sch Mech Engn 80 Daehak Ro Daegu 41566 South Korea Huazhong Univ Sci & Technol Sch Aerosp Engn Wuhan Peoples R China Univ Iowa Dept Mech Engn IIHR Hydrosci & Engn Iowa IA USA Univ Virginia Sch Data Sci Charlottesville VA USA Univ Virginia Dept Mech & Aerosp Engn Charlottesville VA USA Kangwon Natl Univ Kangwon Natl Univ Hosp Dept Internal Med & Environm Hlth Ctr Sch Med Chunchon South Korea Seoul Natl Univ Seoul Natl Univ Hosp Coll Med Dept Radiol Seoul South Korea Univ Iowa Coll Med Dept Radiol Iowa IA USA Jeonbuk Natl Univ Jeonbuk Natl Univ Hosp Dept Radiol Res Inst Clin MedBiomed Res Inst Jeonju South Korea

Background and Objective: A detailed representation of the airway geometry in the respiratory system is critical for predicting precise airflow and pressure behaviors in computed tomography (CT)-image-based computational fluid dynamics (CFD). The CT-image-based geometry often contains artifacts, noise, and discontinuities due to the so-called stair step effect. Hence, an advanced surface smoothing is necessary. The existing smoothing methods based on the Laplacian operator drastically shrink airway geometries, resulting in the loss of information related to smaller branches. This study aims to introduce an unsupervised airway-mesh-smoothing learning (AMSL) method that preserves the original geometry of the three-dimensional (3D) airway for accurate CT-image-based CFD simulations. Method: The AMSL method jointly trains two graph convolutional neural networks (GCNNs) defined on airway meshes to filter vertex positions and face normal vectors. In addition, it regularizes a combination of loss functions such as reproducibility, smoothness and consistency of vertex positions, and normal vectors. The AMSL adopts the concept of a deep mesh prior model, and it determines the self-similarity for mesh restoration without using a large dataset for training. Images of the airways of 20 subjects were smoothed by the AMSL method, and among them, the data of two subjects were used for the CFD simulations to assess the effect of airway smoothing on flow properties. Results: In 18 of 20 benchmark problems, the proposed smoothing method delivered better results compared with the conventional or state-of-the-art deep learning methods. Unlike the traditional smoothing, the AMSL successfully constructed 20 smoothed airways with airway diameters that were consistent with the original CT images. Besides, CFD simulations with the airways obtained by the AMSL method showed much smaller pressure drop and wall shear stress than the results obtained by the traditional method. Conclusions: The

关键词： Surface mesh smoothing Deep mesh prior graph convolutional neural network Computed tomography Computational fluid dynamics

来源：评论

学校读者我要写书评

暂无评论

Applying graph Convolution neural network in Digital Breast Tomosynthesis for Cancer Classification 22

Applying Graph Convolution Neural Network in Digital Breast ...

引用

13th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Bai, Jun Jin, Annie Jin, Andre Wang, Tianyu Yang, Clifford Nabavi, Sheida Univ Connecticut Dept Comp Sicence & Engn Storrs CT 06269 USA Univ Connecticut Sch Med Farmington CT USA Univ Connecticut Dept Elect Engn Storrs CT USA Univ Connecticut Sch Med Dept Radiol UConn Hlth Farmington CT USA

ISBN: (纸本)9781450393867

Digital breast tomosynthesis, or 3D mammography, has advanced the field of breast imaging diagnosis. It has been rapidly replacing the traditional full-field digital mammography because of its diagnostic superiority. However, automatic detection of breast cancer using digital breast tomosynthesis images has remained challenging, mainly due to their high resolution, high volume, and complexity. In this study, we developed a novel model for more precise detection of cancerous 3D mammogram images. The proposed model first, represents 3D mammograms as graphs, then employs a self-attention graph convolutional neural network model to effectively and efficiently learn the features of 3D mammograms, and finally, using the extracted features, identifies the cancerous 3D mammograms. We trained and evaluated the performance of the proposed model using public and private datasets. We compared the performance of the proposed model with those of multiple state-of-the-art CNN-based models as baseline models. The results show that the proposed model outperforms all the baseline models in terms of accuracy, precision, sensitivity, F1, and AUC.

关键词： graph convolutional neural network Digital breast tomosynthesis Deep learning Breast Cancer detection

来源：评论

学校读者我要写书评

暂无评论

Residual connection-based graph convolutional neural networks for gait recognition

引用

VISUAL COMPUTER 2021年第9-11期37卷 2713-2724页

作者： Shopon, Md Bari, A. S. M. Hossain Gavrilova, Marina L. Univ Calgary Comp Sci Calgary AB T2N 1N4 Canada Univ Calgary Calgary AB T2N 1N4 Canada Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada

The walking manner of a person, also known as gait, is a unique behavioral biometric trait. Existing methods for gait recognition predominantly utilize traditional machine learning. However, the performance of gait recognition can deteriorate under challenging conditions including environmental occlusion, bulky clothing, and different viewing angles. To provide an effective solution to gait recognition under these conditions, this paper proposes a novel deep learning architecture using graph convolutional neural network (GCNN) that incorporates residual connections for gait recognition from videos. The optimized feature map of the proposed GCNN architecture exhibits the invariant property to viewing angle and subject's clothing. The residual connection is used to capture both spatial and temporal features of a gait sequence. The kinematic dependency extracted from shallower network layer is propagated to deeper layer using residual connection-based GCNN architecture. The proposed method is validated on CASIA-B gait dataset and outperforms all recent state-of-the-art methods.

关键词： Gait Recognition Behavioral biometric graph convolutional neural network Video Processing

来源：评论

学校读者我要写书评

暂无评论

QSAR modeling without descriptors using graph convolutional neural networks: the case of mutagenicity prediction

引用

MOLECULAR DIVERSITY 2021年第3期25卷 1283-1299页

作者： Hung, Chiakang Gini, Giuseppina Politecn Milan DEIB Milan Italy

Deep neural networks are effective in learning directly from low-level encoded data without the need of feature extraction. This paper shows how QSAR models can be constructed from 2D molecular graphs without computing chemical descriptors. Two graph convolutional neural network-based models are presented with and without a Bayesian estimation of the prediction uncertainty. The property under investigation is mutagenicity: Models developed here predict the output of the Ames test. These models take the SMILES representation of the molecules as input to produce molecular graphs in terms of adjacency matrices and subsequently use attention mechanisms to weight the role of their subgraphs in producing the output. The results positively compare with current state-of-the-art models. Furthermore, our proposed model interpretation can be enhanced by the automatic extraction of the substructures most important in driving the prediction, as well as by uncertainty estimations. [graphICS] .

关键词： Toxicity prediction Ames test Deep learning graph convolutional neural network Bayesian uncertainty Structural alerts

来源：评论

学校读者我要写书评

暂无评论

BugPre: an intelligent software version-to-version bug prediction system using graph convolutional neural networks

引用

COMPLEX & INTELLIGENT SYSTEMS 2023年第4期9卷 3835-3855页

作者： Wang, Zixu Tong, Weiyuan Li, Peng Ye, Guixin Chen, Hao Gong, Xiaoqing Tang, Zhanyong Northwest Univ Sch Informat Sci & Technol Xian 710127 Shaanxi Peoples R China China Univ Lab Relat Beijing 100048 Peoples R China

Since defects in software may cause product fault and financial loss, it is essential to conduct software defect prediction (SDP) to identify the potentially defective modules, especially in the early stage of the software development lifecycle. Recently, cross-version defect prediction (CVDP) began to draw increasing research interests, employing the labeled defect data of the prior version within the same project to predict defects in the current version. As software development is a dynamic process, the data distribution (such as defects) during version change may get changed. Recent studies utilize machine learning (ML) techniques to detect software defects. However, due to the close dependencies between the updated and unchanged code, prior ML-based methods fail to model the long and deep dependencies, causing a high false positive. Furthermore, traditional defect detection is performed on the entire project, and the detection efficiency is relatively low, especially on large-scale software projects. To this end, we propose BugPre, a CVDP approach to address these two issues. BugPre is a novel framework that only conducts efficient defect prediction on changed modules in the current version. BugPre utilizes variable propagation tree-based associated analysis method to obtain the changed modules in the current version. Besides, BugPre constructs graph leveraging code context dependences and uses a graph convolutional neural network to learn representative characteristics of code, thereby improving defect prediction capability when version changes occur. Through extensive experiments on open-source Apache projects, the experimental results indicate that our BugPre outperforms three state-of-the-art defect detection approaches, and the F1-score has increased by higher than 16%.

关键词： Defect detection Change impact analysis Software version changes graph convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

A simple yet effective approach for predicting disease spread using mathematically-inspired diffusion-informed neural networks

引用

Scientific Reports 2025年第1期15卷 1-14页

作者： Jeong, ByeongChang Lee, Yeon Ju Han, Cheol E. Department of Electronics and Information Engineering Korea University Sejong South Korea Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University 2511 Sejong-ro Sejong 30019 South Korea Department of Applied Mathematics Korea University Sejong South Korea

The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter estimation. To address this, we proposed a novel model that integrates traditional mathematical modeling with deep learning which has shown improved predicted power across diverse fields. The proposed model includes a simple artificial neural network (ANN) for regional disease incidences, and a graph convolutional neural network (GCN) to capture spread to adjacent regions. GCNs are a recent deep learning algorithm designed to learn spatial relationship from graph-structured data. We applied the model to COVID-19 incidences in Spain to evaluate its performance. It achieved a 0.9679 correlation with the test data, outperforming previous models with fewer parameters. By leveraging the efficient training methods of deep learning, the model simplifies parameter estimation while maintaining alignment with the mathematical framework to ensure interpretability. The proposed model may allow the more robust and insightful analyses by leveraging the generalization power of deep learning and theoretical foundations of the mathematical models. © The Author(s) 2025.

关键词： Artificial neural networks COVID-19 Deep learning graph convolutional neural network

来源：评论

学校读者我要写书评

暂无评论

Pooling in graph convolutional neural networks 53

Pooling in Graph Convolutional Neural Networks

引用

53rd Asilomar Conference on Signals, Systems, and Computers (ACSSC)

作者： Cheung, Mark Shi, John Jiang, Lavender Wright, Oren Moura, Jose M. F. Carnegie Mellon Univ Elect & Comp Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Inst Software Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781728143002

graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems. We empirically evaluate several pooling methods for GCNNs, and combinations of those graph pooling methods with three different architectures: GCN, TAGCN, and graphSAGE. We confirm that graph pooling, especially DiffPool, improves classification accuracy on popular graph classification datasets and find that, on average, TAGCN achieves comparable or better accuracy than GCN and graphSAGE, particularly for datasets with larger and sparser graph structures.

关键词： graph convolutional neural network graph pooling TAGCN graph classification graph signal processing

来源：评论

学校读者我要写书评

暂无评论

Predicting vibrancy of metro station areas considering spatial relationships through graph convolutional neural networks: The case of Shenzhen, China

引用

ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE 2021年第8期48卷 2363-2384页

作者： Xiao, Longzhu Lo, Siuming Zhou, Jiangping Liu, Jixiang Yang, Linchuan City Univ Hong Kong Dept Architecture & Civil Engn Hong Kong Peoples R China Univ Hong Kong Dept Urban Planning & Design Hong Kong Peoples R China Southwest Jiaotong Univ Dept Urban & Rural Planning Chengdu Peoples R China

Vibrancy is one of the most desirable outcomes of transit-oriented development (TOD). The vibrancy of a metro station area (MSA) depends partially on the MSA's built-environment features. Predicting an MSA's vibrancy with its built-environment features is of great interest to decision makers as these features are often modifiable by public interventions. However, little has been done on MSAs' vibrancy in existing studies. On the one hand, seldom has the vibrancy of MSAs been explicitly explored, and measuring the vibrancy is essential. On the other hand, because MSAs are interconnected, one MSA's vibrancy depends on the MSA's features and those of relevant MSAs. Hence, selecting a suitable metric that quantifies spatial relationships between MSAs can better predict MSAs' vibrancy. In this study, we identify four single-dimensional vibrancy proxies and fuse them into an integrated index. Moreover, we design a two-layer graph convolutional neural network model that accounts for both the built-environment features of MSAs and spatial relationships between MSAs. We employ the model in an empirical study in Shenzhen, China, and illustrate (1) how different metrics of spatial relationships influence the prediction of MSAs' vibrancy;(2) how the predictability varies across single-dimensional and integrated proxies of MSAs' vibrancy;and (3) how the findings of this study can be used to enlighten decision makers. This study enriches our understandings of spatial relationships between MSAs. Moreover, it can help decision makers with targeted policies for developing MSAs towards TOD.

关键词： Metro station area vibrancy graph convolutional neural network spatial relationships Shenzhen China

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：