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Identifying pathological groups from MRI in prostate cancer using graph representation learning

DISPLAYS 2024年 83卷

作者： Liu, Feng Zhao, Yuanshen Yan, Chongzhe Duan, Jingxian Tang, Lei Gao, Bo Wang, Rongpin Guizhou Univ Med Coll Guiyang 550025 Guizhou Peoples R China Guizhou Prov Peoples Hosp Dept Ultrasound Guiyang 550002 Guizhou Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Inst Biomed & Hlth Engn Shenzhen 518000 Peoples R China Guizhou Prov Peoples Hosp Dept Med Imaging Int Exemplary Cooperat Base Precis Imaging Diag & Guiyang 550002 Guizhou Peoples R China Guizhou Med Univ Affiliated Hosp Dept Radiol Guiyang 550004 Peoples R China

Multiparametric magnetic resonance imaging (mpMRI) plays a critical role in prostate cancer (PCa) diagnosis, aiding in clinical trial evaluation and personalized treatment planning. We propose a novel prediction approach based on graph representation learning (GRL) integrating mpMRI images for classifying International Society of Urological Pathology (ISUP) grade groups. We initially constructed a mpMRI image graph representation learning (MMIGRL) model using a graph-based representation fusion approach. After constructing a heterogeneous graph integrating T2-weighted and apparent diffusion coefficient (ADC) images, we applied an adaptive method that dynamically adjusted the edges within the graph to reveal relationships within the mpMRI images. Subsequently, we developed a residual graph convolution network (Res-GCN) model to establish the mapping relationship between the mpMRI fusion graph and ISUP grade groups. The results of 5-fold cross-validation demonstrated excellent overall performance, and the prediction results using independent testing datasets confirmed the model ' s generalizability. The results of ablation studies highlighted the positive impact of integrating T2-weighted and ADC images and the effectiveness of residual connections in learning hierarchical features within the graph structure. The results of comparative experiments validated the superiority of the proposed MMIGRL model compared with convolutional neural network (CNN) and vision transformer (ViT) models. This approach holds promise for accurate ISUP grade group classification in PCa diagnosis, contributing to improved patient care and clinical decision making.

关键词： Prostate cancer Multiparametric MRI International Society of Urological Pathology graph representation learning Residual graph convolution network

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GRLC: graph representation learning With Constraints

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第6期35卷 8609-8622页

作者： Peng, Liang Mo, Yujie Xu, Jie Shen, Jialie Shi, Xiaoshuang Li, Xiaoxiao Shen, Heng Tao Zhu, Xiaofeng Univ Elect Sci & Technol China Ctr Future Media Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Technol Chengdu 611731 Peoples R China City Univ London Dept Comp Sci London EC1V 0HB England Univ British Columbia Dept Elect & Comp Engn Vancouver BC V6T 1Z4 Canada Guangxi Acad Sci Nanning 530007 Peoples R China

Contrastive learning has been successfully applied in unsupervised representation learning. However, the generalization ability of representation learning is limited by the fact that the loss of downstream tasks (e.g., classification) is rarely taken into account while designing contrastive methods. In this article, we propose a new contrastive-based unsupervised graph representation learning (UGRL) framework by 1) maximizing the mutual information (MI) between the semantic information and the structural information of the data and 2) designing three constraints to simultaneously consider the downstream tasks and the representation learning. As a result, our proposed method outputs robust low-dimensional representations. Experimental results on 11 public datasets demonstrate that our proposed method is superior over recent state-of-the-art methods in terms of different downstream tasks. Our code is available at https://***/LarryUESTC/GRLC.

关键词： Task analysis representation learning Semantics Self-supervised learning Training Mutual information Computer science Data mining graph neural networks graph representation learning machine learning

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API Usage Recommendation Via Multi-View Heterogeneous graph representation learning

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IEEE TRANSACTIONS ON SOFTWARE ENGINEERING 2023年第5期49卷 3289-3304页

作者： Chen, Yujia Gao, Cuiyun Ren, Xiaoxue Peng, Yun Xia, Xin Lyu, Michael R. R. Harbin Inst Technol Shenzhen 518055 Guangdong Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Software Engn Applicat Technol Lab Huawei 518129 Peoples R China

Developers often need to decide which APIs to use for the functions being implemented. With the ever-growing number of APIs and libraries, it becomes increasingly difficult for developers to find appropriate APIs, indicating the necessity of automatic API usage recommendation. Previous studies adopt statistical models or collaborative filtering methods to mine the implicit API usage patterns for recommendation. However, they rely on the occurrence frequencies of APIs for mining usage patterns, thus prone to fail for the low-frequency APIs. Besides, prior studies generally regard the API call interaction graph as homogeneous graph, ignoring the rich information (e.g., edge types) in the structure graph. In this work, we propose a novel method named MEGA for improving the recommendation accuracy especially for the low-frequency APIs. Specifically, besides call interaction graph, MEGA considers another two new heterogeneous graphs: global API co-occurrence graph enriched with the API frequency information and hierarchical structure graph enriched with the project component information. With the three multi-view heterogeneous graphs, MEGA can capture the API usage patterns more accurately. Experiments on three Java benchmark datasets demonstrate that MEGA significantly outperforms the baseline models by at least 19% with respect to the Success Rate@1 metric. Especially, for the low-frequency APIs, MEGA also increases the baselines by at least 55% regarding the Success Rate@1 score.

关键词： Codes Java Source coding Benchmark testing Software representation learning Programming API recommendation graph representation learning multi-view heterogeneous graphs

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graph representation learning and Its Applications: A Survey

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SENSORS 2023年第8期23卷 4168页

作者： Hoang, Van Thuy Jeon, Hyeon-Ju You, Eun-Soon Yoon, Yoewon Jung, Sungyeop Lee, O-Joun Catholic Univ Korea Dept Artificial Intelligence 43 Jibong Ro Bucheon 14662 Gyeonggi South Korea Korea Inst Atmospher Predict Syst KIAPS Data Assimilat Grp 35 Boramae Ro 5 Gil Seoul 07071 South Korea Dongguk Univ Dept Social Welf 30 Pildong Ro 1 Gil Seoul 04620 South Korea Seoul Natl Univ Adv Inst Convergence Technol AICT Semicond Devices & Circuits Lab 145 Gwanggyo Ro Suwon 16229 Gyeonggi South Korea

graphs are data structures that effectively represent relational data in the real world. graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc. graph representation learning aims to map graph entities to low-dimensional vectors while preserving graph structure and entity relationships. Over the decades, many models have been proposed for graph representation learning. This paper aims to show a comprehensive picture of graph representation learning models, including traditional and state-of-the-art models on various graphs in different geometric spaces. First, we begin with five types of graph embedding models: graph kernels, matrix factorization models, shallow models, deep-learning models, and non-Euclidean models. In addition, we also discuss graph transformer models and Gaussian embedding models. Second, we present practical applications of graph embedding models, from constructing graphs for specific domains to applying models to solve tasks. Finally, we discuss challenges for existing models and future research directions in detail. As a result, this paper provides a structured overview of the diversity of graph embedding models.

关键词： graph embedding graph representation learning graph transformer graph neural networks

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Hierarchical graph representation learning for the prediction of drug-target binding affinity

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INFORMATION SCIENCES 2022年 613卷 507-523页

作者： Chu, Zhaoyang Huang, Feng Fu, Haitao Quan, Yuan Zhou, Xionghui Liu, Shichao Zhang, Wen Huazhong Agr Univ Coll Informat Wuhan 430070 Peoples R China Huazhong Agr Univ Hubei Engn Technol Res Ctr Agr Big Data Agr Bioinformat Key Lab Hubei Prov Key Lab Smart Anim Farming TechnolMinist Agr Wuhan 430070 Peoples R China

Computationally predicting drug-target binding affinity (DTA) has attracted increasing attention due to its benefit for accelerating drug discovery. Currently, numerous deep learning-based prediction models have been proposed, often with a biencoder architecture that commonly focuses on how to extract expressive representations for drugs and targets but overlooks modeling explicit drug-target interactions. However, known DTA can pro -vide underlying knowledge about how the drugs interact with targets that is beneficial for predictive accuracy. In this paper, we propose a novel hierarchical graph representation learning model for DTA prediction, named HGRL-DTA. The main contribution of our model is to establish a hierarchical graph learning architecture to integrate the coarse-and fine -level information from an affinity graph and drug/target molecule graphs, respectively, in a well-designed coarse-to-fine manner. In addition, we design a similarity-based representa-tion inference method to infer coarse-level information when it is unavailable for new drugs or targets under the cold start scenario. Comprehensive experimental results under four scenarios across two benchmark datasets indicate that HGRL-DTA outperforms the state-of-the-art models in almost all cases.(c) 2022 Elsevier Inc. All rights reserved.

关键词： graph representation learning Binding affinity prediction Hierarchical graph Coarse-to-fine fusion Drug discovery

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Using graph representation learning to Predict Salivary Cortisol Levels in Pancreatic Cancer Patients

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JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2021年第4期5卷 401-419页

作者： Dong, Guimin Boukhechba, Mehdi Shaffer, Kelly M. Ritterband, Lee M. Gioeli, Daniel G. Reilley, Matthew J. Le, Tri M. Kunk, Paul R. Bauer, Todd W. Chow, Philip I. Univ Virginia Engn Syst & Environm 151 Engn Way Charlottesville VA 22901 USA Univ Virginia Sch Med 1215 Lee St Charlottesville VA 22903 USA

Cortisol is a glucocorticoid hormone that is critical to immune system functioning. Studies show that prolonged exposure to high levels of cortisol can lead to a range of physical health ailments including the progression of tumor growth. The ability to monitor cortisol levels over time can therefore be used to facilitate decision-making during cancer treatment. However, collecting serum or saliva samples to monitor cortisol in situ is inconvenient, costly, and impractical. In this paper, we propose a general predictive modeling process that uses passively sensed actigraphy data to predict underlying salivary cortisol levels using graph representation learning. We compare machine learning models with handcrafted feature engineering and with graph representation learning, which includes graph2Vec, Feathergraph, GeoScattering and NetLSD. Our preliminary results generated from data from 10 newly diagnosed pancreatic cancer patients demonstrate that machine learning models with graph representation learning can outperform the handcrafted feature engineering to predict salivary cortisol levels.

关键词： Salivary cortisol Predictive modeling graph representation learning Actigraphy data Mobile sensing

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SaaN 2L-GRL: Two-Level graph representation learning Empowered With Subgraph-as-a-Node

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第12期36卷 9205-9219页

作者： Park, Jeong-Ha Lim, Bo-Young Lee, Kisung Kwon, Hyuk-Yoon Seoul Natl Univ Sci & Technol Grad Sch Data Sci Seoul 01811 South Korea Louisiana State Univ Div Comp Sci & Engn Baton Rouge LA 70803 USA

In this study, we propose a novel graph representation learning (GRL) model, called Two-Level GRL with Subgraph-as-a-Node (SaaN 2L-GRL in short), that partitions input graphs into smaller subgraphs for effective and scalable GRL in two levels: 1) local GRL and 2) global GRL. To realize the two-level GRL in an efficient manner, we propose an abstracted graph, called Subgraph-as-a-Node graph (SaaN in short), to effectively maintain the high-level graph topology while significantly reducing the size of the graph. By applying the SaaN graph to both local and global GRL, SaaN 2L-GRL can effectively preserve the overall structure of the entire graph while precisely representing the nodes within each subgraph. Through time complexity analysis, we confirm that SaaN 2L-GRL significantly reduces the learning time of existing GRL models by using the SaaN graph for global GRL, instead of using the original graph, and processing local GRL on subgraphs in parallel. Our extensive experiments show that SaaN 2L-GRL outperforms existing GRL models in both accuracy and efficiency. In addition, we show the effectiveness of SaaN 2L-GRL using diverse kinds of graph partitioning methods, including five community detection algorithms and representative edge- and vertex-cut algorithms.

关键词： graph partitioning graph representation learning learning efficiency learning efficiency representation accuracy representation accuracy learning efficiency two-level architecture two-level architecture subgraph- as-a-node two-level architecture

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CLEAR: Cluster-Enhanced Contrast for Self-Supervised graph representation learning

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND learning SYSTEMS 2024年第1期35卷 899-912页

作者： Luo, Xiao Ju, Wei Qu, Meng Gu, Yiyang Chen, Chong Deng, Minghua Hua, Xian-Sheng Zhang, Ming Peking Univ Sch Math Sci Beijing 100871 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Univ Montreal Mila Quebec AI Inst Montreal PQ H3T 1J4 Canada Alibaba Grp Discovery Adventure Momentum & Outlook DAMO Acad Hangzhou 311100 Peoples R China

This article studies self-supervised graph representation learning, which is critical to various tasks, such as protein property prediction. Existing methods typically aggregate representations of each individual node as graph representations, but fail to comprehensively explore local substructures (i.e., motifs and subgraphs), which also play important roles in many graph mining tasks. In this article, we propose a self-supervised graph representation learning framework named cluster-enhanced Contrast (CLEAR) that models the structural semantics of a graph from graph-level and substructure-level granularities, i.e., global semantics and local semantics, respectively. Specifically, we use graph-level augmentation strategies followed by a graph neural network-based encoder to explore global semantics. As for local semantics, we first use graph clustering techniques to partition each whole graph into several subgraphs while preserving as much semantic information as possible. We further employ a self-attention interaction module to aggregate the semantics of all subgraphs into a local-view graph representation. Moreover, we integrate both global semantics and local semantics into a multiview graph contrastive learning framework, enhancing the semantic-discriminative ability of graph representations. Extensive experiments on various real-world benchmarks demonstrate the efficacy of the proposed CLEAR over current graph self-supervised representation learning approaches on both graph classification and transfer learning tasks.

关键词： Contrastive learning (CL) graph clustering graph representation learning self-supervised learning

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graph representation learning-Based Early Depression Detection Framework in Smart Home Environments

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SENSORS 2022年第4期22卷 1545页

作者： Kim, Jongmo Sohn, Mye Sungkyunkwan Univ Dept Ind Engn Suwon 16419 South Korea

Although the diagnosis and treatment of depression is a medical field, ICTs and AI technologies are used widely to detect depression earlier in the elderly. These technologies are used to identify behavioral changes in the physical world or sentiment changes in cyberspace, known as symptoms of depression. However, although sentiment and physical changes, which are signs of depression in the elderly, are usually revealed simultaneously, there is no research on them at the same time. To solve the problem, this paper proposes knowledge graph-based cyber-physical view (CPV)-based activity pattern recognition for the early detection of depression, also known as KARE. In the KARE framework, the knowledge graph (KG) plays key roles in providing cross-domain knowledge as well as resolving issues of grammatical and semantic heterogeneity required in order to integrate cyberspace and the physical world. In addition, it can flexibly express the patterns of different activities for each elderly. To achieve this, the KARE framework implements a set of new machine learning techniques. The first is 1D-CNN for attribute representation in relation to learning to connect the attributes of physical and cyber worlds and the KG. The second is the entity alignment with embedding vectors extracted by the CNN and GNN. The third is a graph extraction method to construct the CPV from KG with the graph representation learning and wrapper-based feature selection in the unsupervised manner. The last one is a method of activity-pattern graph representation based on a Gaussian Mixture Model and KL divergence for training the GAT model to detect depression early. To demonstrate the superiority of the KARE framework, we performed the experiments using real-world datasets with five state-of-the-art models in knowledge graph entity alignment.

关键词： smart home early detection of depression (EDD) elderly graph neural networks graph representation learning knowledge graph

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ProcSAGE: an efficient host threat detection method based on graph representation learning

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CYBERSECURITY 2024年第1期7卷 1-14页

作者： Xu, Boyuan Gong, Yiru Geng, Xiaoyu Li, Yun Dong, Cong Liu, Song Liu, Yuling Jiang, Bo Lu, Zhigang Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Zhongguancun Lab Beijing 100194 Peoples R China

Advanced Persistent Threats (APTs) achieves internal networks penetration through multiple methods, making it difficult to detect attack clues solely through boundary defense measures. To address this challenge, some research has proposed threat detection methods based on provenance graphs, which leverage entity relationships such as processes, files, and sockets found in host audit logs. However, these methods are generally inefficient, especially when faced with massive audit logs and the computational resource-intensive nature of graph algorithms. Effectively and economically extracting APT attack clues from massive system audit logs remains a significant challenge. To tackle this problem, this paper introduces the ProcSAGE method, which detects threats based on abnormal behavior patterns, offering high accuracy, low cost, and independence from expert knowledge. ProcSAGE focuses on processes or threads in host audit logs during the graph construction phase to effectively control the scale of provenance graphs and reduce performance overhead. Additionally, in the feature extraction phase, ProcSAGE considers information about the processes or threads themselves and their neighboring nodes to accurately characterize them and enhance model accuracy. In order to verify the effectiveness of the ProcSAGE method, this study conducted a comprehensive evaluation on the StreamSpot dataset. The experimental results show that the ProcSAGE method can significantly reduce the time and memory consumption in the threat detection process while improving the accuracy, and the optimization effect becomes more significant as the data size expands.

关键词： graph representation learning Host threat detection Provenance graph

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