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   The applicability of transperceptual and deep learning approaches to the study and mimicry of complex cartilaginous tissues; ; Hu, Hailong  et alin Frontiers in Materials (2023), 10 Detailed reference viewed: 149 (8 UL)The Burden of Being a Bridge: Analysing Subjective Well-Being of Twitter Users During the COVID-19 PandemicChen, Ninghan ; Chen, Xihui ; Zhong, Zhiqiang et al(2023, March 17) Detailed reference viewed: 95 (2 UL) Target Control of Asynchronous Boolean Networks; Pang, Jun in IEEE/ACM Transactions on Computational Biology and Bioinformatics (2023), 20(1), 707-719 Detailed reference viewed: 63 (0 UL)Relation-aware weighted embedding for heterogeneous graphs; Pang, Jun in Information Technology and Control (2023), 52(1), 199-214 Detailed reference viewed: 79 (1 UL)Multi-grained semantics-aware graph neural networks; ; Pang, Jun in IEEE Transactions on Knowledge and Data Engineering (2023), 35(7), 7251-7262 Detailed reference viewed: 70 (2 UL)Hierarchical message-passing graph neural networks; ; Pang, Jun in Data Mining and Knowledge Discovery (2023), 37 Detailed reference viewed: 104 (3 UL)Unsupervised network embedding beyond homophily; ; et al in Transactions on Machine Learning Research (2022) Detailed reference viewed: 78 (1 UL)Measuring COVID-19 Vaccine Hesitancy: Consistency of Social Media with SurveysChen, Ninghan ; Chen, Xihui ; Pang, Jun et alin Proceedings of the 2022 International Conference on Social Informatics (2022, October 12) Detailed reference viewed: 99 (11 UL)A multilingual dataset of COVID-19 vaccination attitudes on TwitterChen, Ninghan ; Chen, Xihui ; Pang, Jun in Data in Brief (2022), 44 Detailed reference viewed: 118 (9 UL)Simplifying Node Classification on Heterophilous Graphs with Compatible Label Propagation; ; Pang, Jun in Transactions on Machine Learning Research (2022) Detailed reference viewed: 76 (1 UL)A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin; Pang, Jun ; et alin ACM Transactions on the Web (2022), 16(2), 1-29 Detailed reference viewed: 187 (1 UL)Modal characterisation of simulation relations in probabilistic concurrent games; Pang, Jun in Science of Computer Programming (2022), 215 Detailed reference viewed: 127 (1 UL)THS-GWNN: a deep learning framework for temporal network link prediction; Pang, Jun ; in Frontiers of Computer Science (2022), 16(2), 162304 Detailed reference viewed: 203 (2 UL)Exploring Spillover Effects for COVID-19 Cascade PredictionChen, Ninghan ; Chen, Xihui ; Zhong, Zhiqiang et alin Entropy (2022), 24(2), Detailed reference viewed: 139 (17 UL)Target Control of Boolean Networks with Permanent Edgetic PerturbationsZeyen, Olivier Georges Rémy ; Pang, Jun 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 ▲] Detailed reference viewed: 68 (4 UL)Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.Kishk, Ali ; Pires Pacheco, Maria Irene ; Heurtaux, Tony et alin 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 ▲] Detailed reference viewed: 148 (12 UL)Proceedings of the 13th Asia-Pacific Symposium on Internetware; ; et al Book published by ACM (2022) Detailed reference viewed: 104 (0 UL)Effective attributed network embedding with information behavior extraction; Pang, Jun ; in PeerJ Computer Science (2022), 8 Detailed reference viewed: 82 (0 UL)Personalised meta-path generation for heterogeneous graph neural networks; ; Pang, Jun in Data Mining and Knowledge Discovery (2022), 36(6), 2299-2333 Detailed reference viewed: 86 (0 UL)Iterative structural inference of directed graphsWang, Aoran ; Pang, Jun in Proceedings of the 36th Annual Conference on Neural Information Processing Systems (NeurIPS'22) (2022) Detailed reference viewed: 137 (10 UL) |
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