References of "Pang, Jun 50002807"
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See detailThe applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues
Waghorne, Jack; Howard, Cameron; Hu, Hailong UL et al

in Frontiers in Materials (2023), 10

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See detailTarget Control of Asynchronous Boolean Networks
Su, Cui; Pang, Jun UL

in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023), 20(1), 707-719

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See detailHierarchical message-passing graph neural networks
Zhong, Zhiqiang; Li, Cheng-Te; Pang, Jun UL

in Data Mining and Knowledge Discovery (2023), 37

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See detailRelation-aware weighted embedding for heterogeneous graphs
Hu, Ganglin; Pang, Jun UL

in Information Technology and Control (2023), 52(1), 199-214

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See detailUnsupervised network embedding beyond homophily
Zhong, Zhiqiang; Gonzalez, Guadalupe; Grattarola, Daniele et al

in Transactions on Machine Learning Research (2022)

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See detailMeasuring COVID-19 Vaccine Hesitancy: Consistency of Social Media with Surveys
Chen, Ninghan UL; Chen, Xihui UL; Pang, Jun UL et al

in Proceedings of the 2022 International Conference on Social Informatics (2022, October 12)

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See detailA multilingual dataset of COVID-19 vaccination attitudes on Twitter
Chen, Ninghan UL; Chen, Xihui UL; Pang, Jun UL

in Data in Brief (2022), 44

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See detailSimplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation
Zhong, Zhiqiang; Ivanov, Sergey; Pang, Jun UL

in Transactions on Machine Learning Research (2022)

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See detailA Large-scale Empirical Analysis of Ransomware Activities in Bitcoin
Wang, Kai; Pang, Jun UL; Chen, Dingjie et al

in ACM Transactions on the Web (2022), 16(2), 1-29

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See detailModal characterisation of simulation relations in probabilistic concurrent games
Zhang, Chenyi; Pang, Jun UL

in Science of Computer Programming (2022), 215

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See detailTHS-GWNN: a deep learning framework for temporal network link prediction
Mo, Xian; Pang, Jun UL; Liu, Zhiming

in Frontiers of Computer Science (2022), 16(2), 162304

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See detailReview of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.
Kishk, Ali UL; Pires Pacheco, Maria Irene UL; Heurtaux, Tony UL et al

in Cells (2022), 11(16),

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for ... [more ▼]

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition. [less ▲]

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See detailTarget Control of Boolean Networks with Permanent Edgetic Perturbations
Zeyen, Olivier Georges Rémy UL; Pang, Jun UL

in Proceedings of the 61st International Conference on Decision and Control (CDC 2022) (2022)

Boolean network is a popular and well-established modelling framework for gene regulatory networks. The steady-state behaviour of Boolean networks can be described as attractors, which are hypothesised to ... [more ▼]

Boolean network is a popular and well-established modelling framework for gene regulatory networks. The steady-state behaviour of Boolean networks can be described as attractors, which are hypothesised to characterise cellular phenotypes. In this work, we study the target control problem of Boolean networks, which has important applications for cellular reprogramming. More specifically, we want to reduce the total number of attractors of a Boolean network to a single target attractor. Different from existing approaches to solving control problems of Boolean networks with node perturbations, we aim to develop an approach utilising edgetic perturbations. Namely, our objective is to modify the update functions of a Boolean network such that there remains only one attractor. The design of our approach is inspired by Thomas’ first rule, and we primarily focus on the removal of cycles in the interaction graph of a Boolean network. We further use results in the literature to only remove positive cycles which are responsible for the appearance of multiple attractors. We apply our solution to a number of real-life biological networks modelled as Boolean networks, and the experimental results demonstrate its efficacy and efficiency. [less ▲]

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See detailFunctional scenario classification for Android applications using GNNs
Li, Guiyin; Zhu, Fengyi; Pang, Jun UL et al

in Proceedings of the 13th Asia-Pacific Symposium on Internetware (2022)

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See detailIterative structural inference of directed graphs
Wang, Aoran UL; Pang, Jun UL

in Proceedings of the 36th Annual Conference on Neural Information Processing Systems (NeurIPS'22) (2022)

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See detailProceedings of the 13th Asia-Pacific Symposium on Internetware
Mei, Hong; Lv, Jian; Jin, Zhi et al

Book published by ACM (2022)

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See detailEffective attributed network embedding with information behavior extraction
Hu, Ganglin; Pang, Jun UL; Mo, Xian

in PeerJ Computer Science (2022), 8

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See detailPersonalised meta-path generation for heterogeneous graph neural networks
Zhong, Zhiqiang; Li, Cheng-Te; Pang, Jun UL

in Data Mining and Knowledge Discovery (2022), 36(6), 2299-2333

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See detailExploring Spillover Effects for COVID-19 Cascade Prediction
Chen, Ninghan UL; Chen, Xihui UL; Zhong, Zhiqiang UL et al

in Entropy (2022), 24(2),

Detailed reference viewed: 73 (16 UL)