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44th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Tien Huu Do Duc Minh Nguyen Tsiligianni, Evaggelia Aguirre, Angel Lopez La Manna, Valerio Panzica Pasveer, Frank Philips, Wilfried Deligiannis, Nikos Vrije Univ Brussel Pl Laan 2 B-1050 Brussels Belgium IPI Ghent Univ Sint Pietersnieuwstr 25 B-9000 Ghent Belgium Escuela Super Politecn Litoral Km 30-5Via Perimetral Guayaquil Ecuador IMEC Kapeldreef 75 B-3001 Leuven Belgium IMEC Holst Ctr High Tech Campus 31 NL-5656 AE Eindhoven Netherlands

ISBN: (纸本)9781479981311

Inferring air quality from a limited number of observations is an essential task for monitoring and controlling air pollution. Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e.g., meteorological and traffic information. In this work, we focus on street-level air quality inference by utilizing data collected by mobile stations. We formulate air quality inference in this setting as a graph-based matrix completion problem and propose a novel variational model based on graph convolutional autoencoders. Our model captures effectively the spatio-temporal correlation of the measurements and does not depend on the availability of additional information apart from the street-network topology. Experiments on a real air quality dataset, collected with mobile stations, shows that the proposed model out-performs state-of-the-art approaches.

关键词： air quality inference variational graph autoencoder graph-based matrix completion deep learning

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Deep variational graph autoencoders for novel host-directed therapy options against COVID-19

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2022年 134卷 102418-102418页

作者： Ray, Sumanta Lall, Snehalika Mukhopadhyay, Anirban Bandyopadhyay, Sanghamitra Schoenhuth, Alexander Aliah Univ Dept Comp Sci & Engn Kolkata India Hlth Analyt Network Allentown PA USA Univ Bielefeld Genome Data Sci Bielefeld Germany Univ Kalyani Dept Comp Sci & Engn Kalyani India Indian Stat Inst Machine Intelligence Unit Kolkata India

The COVID-19 pandemic has been keeping asking urgent questions with respect to therapeutic options. Existing drugs that can be repurposed promise rapid implementation in practice because of their prior approval. Conceivably, there is still room for substantial improvement, because most advanced artificial intelligence techniques for screening drug repositories have not been exploited so far. We construct a comprehensive network by combining year-long curated drug-protein/protein-protein interaction data on the one hand, and most recent SARS-CoV-2 protein interaction data on the other hand. We learn the structure of the resulting encompassing molecular interaction network and predict missing links using variational graph autoencoders (VGAEs), as a most advanced deep learning technique that has not been explored so far. We focus on hitherto unknown links between drugs and human proteins that play key roles in the replication cycle of SARS-CoV-2. Thereby, we establish novel host-directed therapy (HDT) options whose utmost plausibility is confirmed by realistic simulations. As a consequence, many of the predicted links are likely to be crucial for the virus to thrive on the one hand, and can be targeted with existing drugs on the other hand.

关键词： COVID-19 variational graph autoencoder Node2Vec Molecular interaction network Host directed therapy

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Attention-guided variational graph autoencoders reveal heterogeneity in spatial transcriptomics

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BRIEFINGS IN BIOINFORMATICS 2024年第3期25卷 bbae173-bbae173页

作者： Lei, Lixin Han, Kaitai Wang, Zijun Shi, Chaojing Wang, Zhenghui Dai, Ruoyan Zhang, Zhiwei Wang, Mengqiu Guo, Qianjin Beijing Inst Petrochem Technol Acad Artificial Intelligence 19 Qingyuan North Rd Beijing 102617 Peoples R China

The latest breakthroughs in spatially resolved transcriptomics technology offer comprehensive opportunities to delve into gene expression patterns within the tissue microenvironment. However, the precise identification of spatial domains within tissues remains challenging. In this study, we introduce AttentionVGAE (AVGN), which integrates slice images, spatial information and raw gene expression while calibrating low-quality gene expression. By combining the variational graph autoencoder with multi-head attention blocks (MHA blocks), AVGN captures spatial relationships in tissue gene expression, adaptively focusing on key features and alleviating the need for prior knowledge of cluster numbers, thereby achieving superior clustering performance. Particularly, AVGN attempts to balance the model's attention focus on local and global structures by utilizing MHA blocks, an aspect that current graph neural networks have not extensively addressed. Benchmark testing demonstrates its significant efficacy in elucidating tissue anatomy and interpreting tumor heterogeneity, indicating its potential in advancing spatial transcriptomics research and understanding complex biological phenomena.

关键词： spatially resolved transcriptomics spatial clustering graph deep learning variational graph autoencoder attention-guided

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Predicting miRNA-Disease Associations Based On Multi-View variational graph Auto-Encoder With Matrix Factorization

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2022年第1期26卷 446-457页

作者： Ding, Yulian Lei, Xiujuan Liao, Bo Wu, Fang-Xiang Univ Saskatchewan Div Biomed Engn Saskatoon SK S7N 5A9 Canada Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Hainan Normal Univ Sch Math & Stat Haikou 571158 Hainan Peoples R China Univ Saskatchewan Dept Mech Engn Div Biomed Engn Saskatoon SK S7N 5A9 Canada Univ Saskatchewan Dept Comp Sci Saskatoon SK S7N 5A9 Canada

MicroRNAs (miRNAs) have been proved to play critical roles in diverse biological processes, including the human disease development process. Exploring the potential associations between miRNAs and diseases can help us better understand complex disease mechanisms. Given that traditional biological experiments are expensive and time-consuming, computational models can serve as efficient means to uncover potential miRNA-disease associations. This study presents a new computational model based on variational graph auto-encoder with matrix factorization (VGAMF) for miRNA-disease association prediction. More specifically, VGAMF first integrates four different types of information about miRNAs into an miRNA comprehensive similarity network and two types of information about diseases into a disease comprehensive similarity network, respectively. Then, VGAMF gets the non-linear representations of miRNAs and diseases, respectively, from those two comprehensive similarity networks with variational graph auto-encoders. Simultaneously, a non-negative matrix factorization is conducted on the miRNA-disease association matrix to get the linear representations of miRNAs and diseases. Finally, a fully connected neural network combines linear and non-linear representations of miRNAs and diseases to get the final predicted association score for all miRNA-disease pairs. In the 10-fold cross-validation experiments, VGAMF achieves an average AUC of 0.9280 on HMDD v2.0 and 0.9470 on HMDD v3.2, which outperforms other competing methods. Besides, the case studies on colon cancer and esophageal cancer further demonstrate the effectiveness of VGAMF in predicting novel miRNA-disease associations.

关键词： Diseases Predictive models Databases Computational modeling Biology Biological system modeling Feature extraction miRNA-disease associations non-negative matrix factorization variational graph autoencoder similarity network

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De novo reconstruction of cell interaction landscapes from single-cell spatial transcriptome data with DeepLinc

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GENOME BIOLOGY 2022年第1期23卷 1页

作者： Li, Runze Yang, Xuerui Tsinghua Univ Ctr Synthet & Syst Biol Sch Life Sci MOE Key Lab Bioinformat Beijing 100084 Peoples R China

Based on a deep generative model of variational graph autoencoder (VGAE), we develop a new method, DeepLinc (deep learning framework for Landscapes of Interacting Cells), for the de novo reconstruction of cell interaction networks from single-cell spatial transcriptomic data. DeepLinc demonstrates high efficiency in learning from imperfect and incomplete spatial transcriptome data, filtering false interactions, and imputing missing distal and proximal interactions. The latent representations learned by DeepLinc are also used for inferring the signature genes contributing to the cell interaction landscapes, and for reclustering the cells based on the spatially coded cell heterogeneity in complex tissues at single-cell resolution.

关键词： Single-cell spatial transcriptome Cell interaction variational graph autoencoder VGAE Deep learning

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graphCVAE: Uncovering cell heterogeneity and therapeutic target discovery through residual and contrastive learning

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LIFE SCIENCES 2024年 359卷 123208页

作者： Zhang, Zhiwei Wang, Mengqiu Dai, Ruoyan Wang, Zhenghui Lei, Lixin Zhao, Xudong Han, Kaitai Shi, Chaojing Guo, Qianjin Beijing Inst Petrochem Technol Acad Artificial Intelligence Beijing 102617 Peoples R China

Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often fail to capture spatial dependencies, while spatial clustering methods struggle with batch effects and data integration. We introduce graphCVAE, a model designed to enhance spatial domain identification by integrating spatial and morphological information, correcting batch effects, and managing heterogeneous data. graphCVAE employs a multi-layer graph Convolutional Network (GCN) and a variational autoencoder to improve the representation and integration of spatial information. Through contrastive learning, the model captures subtle differences between cell types and states. Extensive testing on various ST datasets demonstrates graphCVAE's robustness and biological contributions. In the dorsolateral prefrontal cortex (DLPFC) dataset, it accurately delineates cortical layer boundaries. In glioblastoma, graphCVAE reveals critical therapeutic targets such as TF and NFIB. In colorectal cancer, it explores the role of the extracellular matrix in colorectal cancer. The model's performance metrics consistently surpass existing methods, validating its effectiveness. graphCVAE's advanced visualization capabilities further highlight its precision in resolving spatial structures, making it a powerful tool for spatial transcriptomics analysis and offering new insights into disease studies.

关键词： Spatial transcriptomics variational graph autoencoder Contrastive learning Spatial clustering Deep learning Gene expression

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SCIENTOMETRICS 2021年第9期126卷 7525-7560页

作者： Pooja, KM. Mondal, Samrat Chandra, Joydeep Indian Inst Technol Patna Dept Comp Sci & Engn Patna 801103 Bihar India

In bibliometric analysis, ambiguity in author names may lead to erroneous aggregation of records. The author name disambiguation techniques attempt to address this issue by attributing records to the corresponding author. The name disambiguation has been widely studied as a clustering task. However, maintaining consistent accuracy levels over datasets is still a major challenge. Recent efforts have witnessed the use of representation learning based techniques to map the records to an embedding space that can be used to determine the clusters. However, some of these models that use supervised global embedding fail to generalize across different datasets, while others lag in the accuracy. In this paper, we propose a method that uses two independent relations among the documents-co-authorship and meta-content of document, to generate a latent representation of documents that is capable of generalizing over various datasets (consisting different sets of features). Through rigorous validation, we discover that the proposed approach outperforms several state-of-the-art methods by a significant margin in terms of standard measures like pairwise F1, K metric, and BF1 scores. Moreover, we have also validated the performance of our method with the statistical test.

关键词： Author name disambiguation variational graph autoencoder Network embedding Co-author Meta-content

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Estimating Locations of Social Media Content through a graph-based Link Prediction 19

Estimating Locations of Social Media Content through a Graph...

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13th Workshop on Geographic Information Retrieval (GIR)

作者： Liu, Pengyuan De Sabbata, Stefano Univ Leicester Leicester Leics England

ISBN: (纸本)9781450372602

The increasing availability of GPS-enabled devices and social media platforms has led to an increasing interest in mining geolocated content. However, our understanding of the role played by social media in the social construction of place has been limited by the fact that only a small percentage of social media posts are geolocated. Spatio-temporal modelling research in this field so far has mainly focused on analysing the behaviour of single users and predicting user movement patterns based on posts and check-in activities. In this paper, we focus instead on harnessing the dynamics of overall content production from multiple users in a single place to estimate the location of new non-geotagged content. Our proposed location prediction framework uses a variational graph autoencoder, and it allows us to estimate the geolocations of posts based on the semantic understandings of their contents and their topological structure.

关键词： variational graph autoencoder Location Prediction Social Media

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Bitcoin Data Analytics: Scalable techniques for transaction clustering and embedding generation

Bitcoin Data Analytics: Scalable techniques for transaction ...

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International Conference on COMmunication Systems and NETworkS (COMSNETS)

作者： Shah, Raj Sanjay Bhatia, Ashutosh Gandhi, Atith Mathur, Shray Birla Inst Thchnol & Sci Pilani Dept Comp Sci & Informat Syst Pilani Rajasthan India

ISBN: (纸本)9781728191270

Bitcoin provides pseudo-anonymity to its users, leading to many transactions related to illicit activities. The advent of mixing services like OnionBC, Bitcoin Fog, and *** has allowed users to increase their anonymity further. This paper tackles the pseudo-anonymity of the Bitcoin blockchain by developing a scalable spark based framework to find patterns in the transaction data. The efficacy of the framework is demonstrated by performing exploratory analysis. Furthermore, the paper shows the capabilities of bitcoin-based graph representations and addresses the issue of user profiling based on unsupervised learning approaches for analysing Bitcoin transactions and users. The authors convert the transaction graph of the Bitcoin data to contain only Wallet-IDs and generate graph embeddings using variational graph autoencoder [1]. Additionally, the authors use explainable-AI techniques and Kohonen self organizing maps to visualize and understand the results obtained from the unsupervised learning methods.

关键词： bitcoin de-anonymization graph Convolutional Networks variational graph autoencoder Self Organizing Maps Apache Spark

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On Leveraging variational graph Embeddings for Open World Compositional Zero-Shot Learning 22

On Leveraging Variational Graph Embeddings for Open World Co...

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30th ACM International Conference on Multimedia (MM)

作者： Anwaar, Muhammad Umer Pan, Zhihui Kleinsteuber, Martin Unite Network SE Leipzig Germany Tech Univ Munich Leipzig Germany

ISBN: (纸本)9781450392037

Humans are able to identify and categorize novel compositions of known concepts. The task in Compositional Zero-Shot learning (CZSL) is to learn composition of primitive concepts, i.e. objects and states, in such a way that even their novel compositions can be zero-shot classified. In this work, we do not assume any prior knowledge on the feasibility of novel compositions ***-world setting, where infeasible compositions dominate the search space. We propose a Compositional variational graph autoencoder (CVGAE) approach for learning the variational embeddings of the primitive concepts (nodes) as well as feasibility of their compositions (via edges). Such modelling makes CVGAE scalable to real-world application scenarios. This is in contrast to SOTA method, CGE [33], which is computationally very expensive. *** benchmark C-GQA dataset, CGE requires 3.94x10(5) nodes, whereas CVGAE requires only 1323 nodes. We learn a mapping of the graph and image embeddings onto a common embedding space. CVGAE adopts a deep metric learning approach and learns a similarity metric in this space via bi-directional contrastive loss between projected graph and image embeddings. We validate the effectiveness of our approach on three benchmark *** also demonstrate via an image retrieval task that the representations learnt by CVGAE are better suited for compositional generalization.

关键词： Compositional Learning Multimodal variational graph autoencoder CZSL Open World Composition of Concepts

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