Graph Neural Networks for Digital Pathology by Vincenzo DELLA MEA, Hafsa AKEBLI, Kevin ROITERO

“…The graph learning tasks can be either node-level, edge-level or graph-level. …”
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WirelessNet: An Efficient Radio Access Network Model Based on Heterogeneous Graph Neural Networks by Jose Perdomo, M. A. Gutierrez-Estevez, Chan Zhou, Jose F. Monserrat

“…Towards enabling a network digital twin of mobile networks, accurate and efficient radio access network models are needed. In this work, we present WirelessNet, a novel radio access network model based on Heterogeneous Message Passing Graph Neural Networks (HMPGNNs). …”
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Semantic ECG hash similarity graph by Yixian Fang, Shilin Zhang, Yuwei Ren

“…Additionally, the adjacency matrix is highly susceptible to noise interference, leading to unreliable node connections. In this paper, we present a novel graph generation learning framework that incorporates semantic hash coding to capture the intricate associations both within and between ECG signals, thereby significantly enhancing the retrieval efficiency of subsequent graph-based deep learning models. …”
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GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts by Haochen Chen, Tao Liang, Kai Tan, Anan Wu, Xin Lu

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Metastable Substructure Embedding and Robust Classification of Multichannel EEG Data Using Spectral Graph Kernels by Rashmi N. Muralinath, Vishwambhar Pathak, Prabhat K. Mahanti

“…The classification workflow combining mPLV connectivity measure, WL graph Koopman kernel, and Decision Tree (DT) outperformed the alternative combinations, particularly considering the accuracy (91.7%) and F1-score (88.9%), The comparative investigation presented in results section convinces that employing cost-sensitive learning improved the F1-score for the mPLV-WL-DT workflow to 91% compared to 88.9% without cost-sensitive learning. …”
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Deep learning-based multimodal trajectory prediction methods for autonomous driving: state of the art and perspectives by Jun HUANG, Yonglin TIAN, Xingyuan DAI, Xiao WANG, Zhixing PING

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Interpretable deep learning of single-cell and epigenetic data reveals novel molecular insights in aging by Zhi-Peng Li, Zhaozhen Du, De-Shuang Huang, Andrew E. Teschendorff

“…We develop an advanced multi-view graph-level representation learning (MGRL) framework that integrates prior biological network information, to build molecular aging clocks at cell-type resolution, which we subsequently interpret using XAI. …”
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DiGraph-Enabled Digital Twin and Label-Encoding Machine Learning for SCADA Network’s Cyber Attack Analysis in Industry 5.0 by Nabeel Al-Qirim, Anoud Bani-Hani, Munir Majdalawieh, Hussam Al Hamadi, Mohammad Kamrul Hasan

“…Furthermore, the string nature of these affected components’ data makes it challenging to incorporate into machine-learning-enabled intelligence (CTI) processes. To visualize the attacking flow of FDIA, RTCI, and SRA cyber-attacks on SCADA networks, this paper presents a novel “Digital Twin and Machine Learning empowered Cyber Attacking Flow Analysis (DT-ML-CAFA)” approach for grid CTI in Industry 5.0. …”
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Angus: efficient active learning strategies for provenance based intrusion detection by Lin Wu, Yulai Xie, Jin Li, Dan Feng, Jinyuan Liang, Yafeng Wu

“…We present Angus, an active learning framework for provenance-based intrusion detection. …”
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Improving healthy food recommender systems through heterogeneous hypergraph learning by Jing Wang, Jincheng Zhou, Muammer Aksoy, Nidhi Sharma, Md Arafatur Rahman, Jasni Mohamad Zain, Mohammed J.F. Alenazi, Aliyeh Aminzadeh

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Deep learning-based action recognition for analyzing drug-induced bone remodeling mechanisms by Li Qinsheng, Li Ming, Li Yuening, Zhao Xiufeng

“…Traditional experimental and computational approaches often fail to capture this dynamic and multi-scale nature, particularly when influenced by pharmacological agents, which can have both therapeutic and adverse effects.MethodsIn this work, we present a novel deep learning-based framework for action recognition, specifically designed to analyze drug-induced bone remodeling mechanisms. …”
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DeepMoIC: multi-omics data integration via deep graph convolutional networks for cancer subtype classification by Jiecheng Wu, Zhaoliang Chen, Shunxin Xiao, Genggeng Liu, Wenjie Wu, Shiping Wang

“…Results To address the challenges of multi-omics research, our approach DeepMoIC presents a novel framework derived from deep Graph Convolutional Network (GCN). …”
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ToxDL 2.0: Protein toxicity prediction using a pretrained language model and graph neural networks by Lin Zhu, Yi Fang, Shuting Liu, Hong-Bin Shen, Wesley De Neve, Xiaoyong Pan

“…Results: In this study, we present ToxDL 2.0, a novel multimodal deep learning model for protein toxicity prediction that integrates both evolutionary and structural information derived from a pretrained language model and AlphaFold2. …”
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DFL topology optimization based on peer weighting mechanism and graph neural network in digital twin platform by Nguyen Anh Tuan, Atif Rizwan, Sa Jim Soe Moe, Anam Nawaz Khan, Do Hyeun Kim

“…For the first time, in this paper, we present a topology optimization framework for DFL that integrates a peer weighting mechanism with graph neural networks (GNNs) within a digital twin platform. …”
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Smart distributed data factory volunteer computing platform for active learning-driven molecular data acquisition by Tsolak Ghukasyan, Vahagn Altunyan, Aram Bughdaryan, Tigran Aghajanyan, Khachik Smbatyan, Garegin A. Papoian, Garik Petrosyan

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Graph-aware isomorphic attention for adaptive dynamics in transformers by Markus J. Buehler

“…We present an approach for modifying transformer architectures by integrating graph-aware relational reasoning into the attention mechanism, merging concepts from graph neural networks and language modeling. …”
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kMoL: an open-source machine and federated learning library for drug discovery by Romeo Cozac, Haris Hasic, Jun Jin Choong, Vincent Richard, Loic Beheshti, Cyrille Froehlich, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, Hiroaki Iwata, Aki Hasegawa, Takao Otsuka, Yasushi Okuno

“…Additionally, the experiment results provide further insights into the performance trade-offs associated with federated learning strategies, presenting valuable guidance for deploying machine learning models in a privacy-preserving manner within drug discovery pipelines.…”
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Influence maximization under imbalanced heterogeneous networks via lightweight reinforcement learning with prior knowledge by Kehong You, Sanyang Liu, Yiguang Bai

“…In this work, we introduce the Lightweight Reinforcement Learning algorithm with Prior knowledge (LRLP), which leverages the Struc2Vec graph embedding technique that captures the structural similarity of nodes to generate vector representations for nodes within the network. …”
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Network-based analyses of multiomics data in biomedicine by Rachit Kumar, Joseph D. Romano, Marylyn D. Ritchie

“…This review will present various existing approaches in using network representations and analysis of data in multiomics in the framework of deep learning and machine learning approaches, subdivided into supervised and unsupervised approaches, to identify benefits and drawbacks of various approaches as well as the possible next steps for the field.…”
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Open-World Semi-Supervised Learning for fMRI Analysis to Diagnose Psychiatric Disease by Chang Hu, Yihong Dong, Shoubo Peng, Yuehan Wu

“…In the clinical diagnosis of mental disorders, there often arises a problem of limited labeled data due to factors such as large data volumes and cumbersome labeling processes, leading to the emergence of unlabeled data with new classes, which can result in misdiagnosis. In the context of graph-based mental disorder classification, open-world semi-supervised learning for node classification aims to classify unlabeled nodes into known classes or potentially new classes, presenting a practical yet underexplored issue within the graph community. …”
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