Identifying and classifying trait linked polymorphisms in non-reference species by walking coloured de bruijn graphs. by Richard M Leggett, Ricardo H Ramirez-Gonzalez, Walter Verweij, Cintia G Kawashima, Zamin Iqbal, Jonathan D G Jones, Mario Caccamo, Daniel Maclean

“…Bubbleparse uses the de Bruijn graph implementation in the Cortex framework as a basis and allows the user to identify bubbles in these graphs that represent polymorphisms, quickly, easily and sensitively. …”
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Accuracy of StepWatch™ and ActiGraph accelerometers for measuring steps taken among persons with multiple sclerosis. by Brian M Sandroff, Robert W Motl, Lara A Pilutti, Yvonne C Learmonth, Ipek Ensari, Deirdre Dlugonski, Rachel E Klaren, Swathi Balantrapu, Barry J Riskin

“…The StepWatch had better accuracy (99.0%) than the ActiGraph (95.5%) in the overall sample under the SWS condition, and this was particularly apparent in those with severe disability (StepWatch: 95.7%; ActiGraph: 87.3%). …”
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Calibrating calving parameterizations using graph neural network emulators: application to Helheim Glacier, East Greenland by Y. Koo, Y. Koo, Y. Koo, G. Cheng, M. Morlighem, M. Rahnemoonfar, M. Rahnemoonfar

“…Moreover, these emulators exhibit uncertainties of less than 10 <span class="inline-formula">%</span>–20 <span class="inline-formula">%</span> when extrapolating to out-of-sample calving parameterization cases. Among the three GNN architectures, EGCN outperforms the others by preserving the equivariance of graph structures. …”
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MS_HGNN: a hybrid online fraud detection model to alleviate graph-based data imbalance by Jing Long, Fei Fang, Cuiting Luo, Yehua Wei, Tien-Hsiung Weng

“…The existing fraud detection methods will solve the class imbalance by sampling, but they do not fully consider the various imbalances in the heterogeneous graph, and the data imbalance will directly affect the performance of the model. …”
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Research on Urban Road Traffic Flow Prediction Based on Sa-Dynamic Graph Convolutional Neural Network by Song Hu, Jian Gu, Shun Li

“…However, most existing GNN-based methods apply a fixed graph structure to capture spatial dependencies between nodes, and fixed graph structures may not be able to reflect the spatiotemporal changes in node dependencies. …”
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Effective integration of multi-omics with prior knowledge to identify biomarkers via explainable graph neural networks by Rohit K. Tripathy, Zachary Frohock, Hong Wang, Gregory A. Cary, Stephen Keegan, Gregory W. Carter, Yi Li

“…Such methods are essential for building predictive models and identifying drug targets based on a limited number of samples. We propose a framework called GNNRAI for the supervised integration of multi-omics data with biological priors represented as knowledge graphs. …”
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L2-GNN: Graph neural networks with fast spectral filters using twice linear parameterization by Siying Huang, Xin Yang, Zhengda Lu, Hongxing Qin, Huaiwen Zhang, Yiqun Wang

“…To improve learning on irregular 3D shapes, such as meshes with varying discretizations and point clouds with different samplings, we propose L2-GNN, a new graph neural network that approximates the spectral filters using twice linear parameterization. …”
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MVHGCN: Predicting circRNA-disease associations with multi-view heterogeneous graph convolutional neural networks. by Yan Miao, Xuan Tang, Chunyu Wang, Zhenyuan Sun, Guohua Wang, Shan Huang

“…MVHGCN first constructs a heterogeneous graph and generates feature descriptors by integrating multiple databases. …”
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FLC-Net: Innovative fraud classification in loan applications with dual channel graph advanced neural network by P. Pedda Sadhu Naik, Kalleda Appanolla Architha, Maddela Mahesh, Chitkula Anand, Gaddam Shriya Reddy, Patlola Suraj Reddy

“…Adaptive synthesis (AdaSyn) feature balancing is utilized to tackle the data imbalance issue, ensuring minority class representation by generating synthetic samples close to genuine instances. The classification framework incorporates a Dual Channel Graph Advanced Neural Network (DCG-ANN), which processes two graph-structured data channels to capture local and global loan feature correlations. …”
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Multi‐omics graph convolutional networks for digestive system tumour classification and early‐late stage diagnosis by Lin Zhou, Zhengzhi Zhu, Hongbo Gao, Chunyu Wang, Muhammad Attique Khan, Mati Ullah, Siffat Ullah Khan

“…The MGTCN model incorporates the Graph Transformer Layer framework to meticulously transform the multi‐omics adjacency matrix, thereby illuminating potential associations among diverse samples. …”
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A Novel Methodology to Develop Mining Stope Stability Graphs on Imbalanced Datasets Using Probabilistic Approaches by Lucas de Almeida Gama Paixao, William Pratt Rogers, Erisvaldo Bitencourt de Jesus

“…The approach includes rebalancing of the dataset using the Synthetic Minority Over-Sampling Technique (SMOTE) and feature selection using permutation importance to identify key features that impact instability, using those to construct a bi-dimensional stability graph that provides both improved performance and interpretability. …”
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Synthesizing Remote Sensing Images from Land Cover Annotations via Graph Prior Masked Diffusion by Kai Deng, Siyuan Wei, Shiyan Pang, Huiwei Jiang, Bo Su

“…To address this challenge, we propose GMDiT, an enhanced conditional diffusion model that extends the masked DiT architecture with graph-prior modeling. By jointly incorporating relational graph structures and semantic labels, GMDiT explicitly captures the object-level spatial and semantic dependencies, thereby improving the contextual coherence and structural fidelity of the synthesized images. …”
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PhishingGNN: Phishing Email Detection Using Graph Attention Networks and Transformer-Based Feature Extraction by Mejdl Safran, Abdulbaset Musleh

“…By transforming email bodies into relational graphs, PhishingGNN leverages Graph Neural Networks (GNNs) to analyze textual interactions while retaining computational efficiency. …”
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Why and when you should avoid using z-scores in graphs displaying profile or group differences by Julia Moeller

“… (4) The psychological meaning of a given z-score does not compare across samples and variables. (5) Group assignments can be misleading if z-scores are used to assign individuals to groups…”
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An improved graph neural network integrating indicator attention and spatio-temporal correlation for dissolved oxygen prediction by Fei Ding, Shilong Hao, Mingcen Jiang, Hongfei Liu, Jingjie Wang, Bing Hao, Haobin Yuan, Hanjie Mao, Yang Hu, Wenpan Li, Xin Xie, Yong Zhang

“…Hourly water quality data at 20 sampling sites in the Chaohu Lake basin from January 2022 to February 2024 were used as the research dataset. …”
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A Cascaded Ensemble Framework Using BERT and Graph Features for Emotion Detection From English Poetry by Praveen K. Kazipeta, Venkatrama Phani Kumar Sistla, Venkata Krishna Kishore Kolli

“…This work performs feature fusion only in case of failed input samples. The performance of the proposed model is evaluated and compared against four baseline models using word embedding models, including Glove and Fast Text. …”
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Research on safety risk assessment model of construction engineering based on attention mechanism and graph neural network by Lanfei He, Ran Chen, Jia Hu, Zhenxi Huang, Li Zhou, Hong Zhang

“…The comprehensive evaluation of multi-dimensional and multi-level risks of construction projects is realized by constructing an evaluation model that combines attention mechanism and graph neural network. In terms of data analysis, this paper uses the historical data of several actual construction projects as training and test samples, covering many key risk areas such as construction period, quality, and capital. …”
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Conditional Generation of Building Bubble Diagrams Based on Stochastic Differential Equations by Zhiwen Wei, Joonki Lee, Hyeongmo Gu, Seungyeon Choo, Jaeil Kim

“…The trained score-based model enables conditional sampling from pure noise to generate new diagrams. …”
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Fine Sieving of Atmospheric Particles in a Collected Air Sample Using Oil Electrophoresis by Xinyue Li, Siyu Xu, Maosheng Yao

“…The diverse origins of the sample—ambient air, soil, or road dust—exhibited specific charged properties, and clearly affected the electrical mobility, as demonstrated by the graphs, of the particles following the “iSCAPEing,” which lasted from seconds to minutes. …”
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A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data by Kinh Bac Dang, Van Bao Dang, Quang Thanh Bui, Van Vuong Nguyen, Thi Phuong Nga Pham, Van Liem Ngo

“…Eight hundred coastal samples representing eight types of coasts taken along the coastline in Vietnam were used for training and testing various ConvNets. …”
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