Article
Biochemical Research Methods
Lijun Cai, Changcheng Lu, Junlin Xu, Yajie Meng, Peng Wang, Xiangzheng Fu, Xiangxiang Zeng, Yansen Su
Summary: The study introduces a novel method for drug repositioning based on graph convolutional network, which effectively discovers potential drugs. By designing feature extraction modules and attention mechanism, higher prediction performance is achieved. Experiments demonstrate the superior performance of this method in multiple benchmark datasets, identifying several novel drugs for disease treatment.
BRIEFINGS IN BIOINFORMATICS
(2021)
Article
Health Care Sciences & Services
Jingyun Tang, Guang Yu, Xiaoxu Yao
Summary: The research found that emotional contagion exists in the online depression community, where users' emotions can be easily influenced by others. Positive emotions are more easily spread, and individuals with higher activity levels are more susceptible; nighttime is a period of high user interaction activity.
Article
Biochemical Research Methods
Bo-Wei Zhao, Xiao-Rui Su, Peng-Wei Hu, Yu-Peng Ma, Xi Zhou, Lun Hu
Summary: Drug repositioning is a strategy that uses artificial intelligence techniques to discover new indicators for approved drugs and improve traditional drug discovery and development. However, most computational methods fail to consider the non-Euclidean nature of biomedical network data. To address this, a deep learning framework called DDAGDL is proposed to predict drug-drug associations. Experimental results show that this method outperforms state-of-the-art drug repositioning methods in terms of several evaluation metrics.
BRIEFINGS IN BIOINFORMATICS
(2022)
Article
Pharmacology & Pharmacy
Song Lei, Xiujuan Lei, Lian Liu
Summary: The drug repositioning method VGAEDR is based on a heterogeneous network and a variational graph autoencoder, and it predicts new drug-disease associations by learning low-dimensional feature representations. Comparative experiments demonstrate the excellent performance of VGAEDR, and it has achieved success in the case study of COVID-19 drug repositioning.
FRONTIERS IN PHARMACOLOGY
(2022)
Review
Biochemistry & Molecular Biology
Yoonbee Kim, Yi-Sue Jung, Jong-Hoon Park, Seon-Jun Kim, Young-Rae Cho
Summary: Drug repositioning, utilizing heterogeneous networks, is an effective approach to identify new therapeutic indications for approved drugs. This review summarizes network-based methods, including graph mining, matrix factorization, and deep learning, for predicting drug-disease associations. A comparison of predictive performances was conducted, revealing that methods in the graph mining and matrix factorization categories performed well overall.
Article
Computer Science, Information Systems
Ying Ji, Guojia Wan, Yibing Zhan, Bo Du
Summary: In this paper, we propose a method to model a molecule as a heterogeneous graph and leverage metapaths to capture latent features for chemical functional groups. We construct metapath-based connectivity and decompose the heterogeneous graph into subgraphs according to relation types. A hierarchical attention strategy is designed to aggregate heterogeneous information at the node and relation level. Experimental results show the effectiveness of our model with competitive performance.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Yongliang Wu, Yue Fu, Jiwei Xu, Hu Yin, Qianqian Zhou, Dongbo Liu
Summary: In this paper, a Heterogeneous Community Detection Approach Based on Graph Neural Network (HCDBG) is proposed to detect heterogeneous communities in Community Question Answering (CQA) platforms. The approach defines entity relationships based on user interaction behavior and employs a heterogeneous information network to represent all connections. The graph neural network is then utilized to fuse content and topological features for graph embedding. The approach outperforms baseline methods in heterogeneous community detection.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Information Systems
Michele Coscia
Summary: The article introduces a new method for topological network sampling, which selects the next node to explore by following the edge that provides the largest information gain. The method performs well across a wide range of network topologies and API system features, although it may not provide the best sample in all scenarios.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Article
Psychology, Multidisciplinary
Chunhui You, Yang Liu
Summary: As social media addiction becomes a growing concern, this study examines coping behaviors and the role of mindfulness in dealing with social media pressure and addiction in China's unique political environment. The findings suggest that mindfulness influences people's socially addictive behaviors and ability to withstand social media pressure. Additionally, individuals are more likely to shift from social media to offline interactions as they perceive higher social media pressure, but their willingness to return to social media platforms increases as they engage in more offline interactions.
FRONTIERS IN PSYCHOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Xueqi Jia, Jiaxing Shang, Dajiang Liu, Haidong Zhang, Wancheng Ni
Summary: This article proposes a graph neural network-based framework named HeDAN, which comprehensively considers various factors affecting information diffusion to provide more accurate prediction results.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Interdisciplinary Applications
Zainab Al-Taie, Danlu Liu, Jonathan B. Mitchem, Christos Papageorgiou, Jussuf T. Kaifi, Wesley C. Warren, Chi-Ren Shyu
Summary: Enabling precision medicine involves developing robust patient stratification methods and drugs tailored to homogeneous subgroups of patients from heterogeneous populations. Drug repositioning is an essential alternative for developing new drugs for disease subpopulations.
JOURNAL OF BIOMEDICAL INFORMATICS
(2021)
Article
Business
So Won Jeong, Sejin Ha, Kyu-Hye Lee
Summary: This research empirically compares three different social capital scales in the context of online brand communities, finding that social capital mainly consists of three dimensions: social interaction ties, trust, and shared value. The study supports Lin and Lu's scale over the others. The investigation provides managerial and theoretical implications by identifying and validating context-specific measures of social capital.
JOURNAL OF BUSINESS RESEARCH
(2021)
Article
Computer Science, Artificial Intelligence
Marco De Luca, Anna Rita Fasolino, Antonino Ferraro, Vincenzo Moscato, Giancarlo Sperli, Porfirio Tramontana
Summary: In this paper, a novel heterogeneous graph-based model is proposed to capture and handle the complex and strongly-correlated information of a software Developer Social Network (DSN) for analytic tasks. The problem of automatically discovering communities of software developers sharing interests for similar projects is addressed using Social Network Analysis (SNA) findings, and graph embedding techniques are utilized to overcome the large graph size. The proposed approach is evaluated against state-of-the-art approaches in terms of efficiency and effectiveness using the GitHub dataset.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Education & Educational Research
Wangda Zhu, Ying Hua, Gaoxia Zhu, Luping Wang
Summary: This study explores the inclusiveness of an Instagram-based learning community and finds that when participants share and discuss environmental psychology concepts and their surroundings on social media, the community is inclusive in terms of gender, ethnicity, and program. The centrality and influence of student participants are related to how they express their identities.
BRITISH JOURNAL OF EDUCATIONAL TECHNOLOGY
(2022)
Article
Mathematics, Applied
Ying Ying Keng, Kiam Heong Kwa, Kurunathan Ratnavelu
Summary: The study demonstrates the significance of central drugs in a drug network for drug repositioning, suggesting that top central drugs are more likely to repurpose their neighboring drugs as new treatment options. This research provides novel insights into complementing drug repositioning efforts and highlights the importance of network centrality measures in guiding systematic analysis.
APPLIED MATHEMATICS AND COMPUTATION
(2021)
Article
Computer Science, Artificial Intelligence
Qianghua Liu, Yu Tian, Tianshu Zhou, Kewei Lyu, Ran Xin, Yong Shang, Ying Liu, Jingjing Ren, Jingsong Li
Summary: This study proposes a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML). It significantly improves the diagnostic process in primary health care and helps general practitioners diagnose few-shot diseases more accurately.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Balazs Borsos, Corinne G. Allaart, Aart van Halteren
Summary: The study demonstrates the feasibility of predicting functional outcomes for ischemic stroke patients and the usability of multimodal deep learning architectures for this purpose.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
Article
Computer Science, Artificial Intelligence
Abdelmoniem Helmy, Radwa Nassar, Nagy Ramdan
Summary: This study utilizes machine learning models to detect depression symptoms in Arabic and English texts, and provides manually and automatically annotated tweet corpora. The study also develops an application that can detect tweets with depression symptoms and predict depression trends.
ARTIFICIAL INTELLIGENCE IN MEDICINE
(2024)
