GraphPI: Efficient Protein Inference with Graph Neural Networks.

Past events have revealed that widespread blackouts are mostly a result of cascading failures in the power grid. Understanding the underlining mechanisms of cascading failures can help in developing strategies to minimize the risk of such events. Moreover, a real-time detection of precursors to cascading failures will help operators take measures to prevent their propagation. Currently, the well-established probabilistic and physics-based models of cascading failures offer low computational efficiency, hindering them to be used only as offline tools. In this work, we develop a data-driven methodology for online estimation of the risk of cascading failures. We utilize a physics-based cascading failure model to generate a cascading failure dataset considering different operating conditions and failure scenarios, thus obtaining a sample space covering a large set of power grid states that are labeled as safe or unsafe. We use the synthetic data to train deep learning architectures, namely Feed-forward Neural Networks (FNN) and Graph Neural Networks (GNN). With the development of GNNs, improved performance is achieved with graph-structured data, and GNNs can generalize to graphs of diverse sizes. A comparison between FNN and GNN is made and the GNNs inductive capability is demonstrated via test grids. Furthermore, we apply transfer learning to improve the performance of a pre-trained GNN model on power grids not seen in the training process. The GNN model shows accuracy and balanced accuracy above 96% on selected test datasets not used in the training. Conversely, the FNN shows accuracy above 85% and balanced accuracy above 81% on test datasets unseen during training. Overall, the GNN model is successful in determining, if one or several simultaneous outages result in a critical grid state, under specific grid operating conditions.

This focus issue of Physiological Measurement follows the 38th Annual International Computing in Cardiology (CinC) Conference, hosted in Hangzhou, China in September 2011 by Zhejiang University. Each year, the NIH-sponsored PhysioNet resource (http://physionet.org/) runs an open competition lasting several months, aimed at encouraging the development of solutions to an unsolved or poorly solved problem in biomedicine, in most cases making use of relevant clinical and experimental data provided freely by PhysioNet. Participants in these annual challenges discuss their diverse approaches to the Challenge problems during dedicated scientific sessions at CinC. The topics of these PhysioNet/CinC Challenges range from physiologic signal processing and analysis to forecasting and modelling clinically important events and processes.

Music style classification plays a fundamental role in music recommendation, retrieval, and organization systems. Traditional classification models primarily rely on audio features or symbolic representations, such as mel-frequency cepstral coefficients or Musical Instrument Digital Interface (MIDI) sequences. However, these models often ignore the rich structural and relational information inherent in musical compositions. This study proposes a novel graph neural network (GNN)-based framework for music style classification that represents each piece of music as a graph, capturing the relationships among notes, chords, and temporal transitions. By modeling these components as interconnected nodes, the GNN is able to learn stylistic features that extend beyond local patterns, such as harmonic progressions, motif repetitions, and inter-note dependencies. To enhance model performance, the framework incorporates a dual-graph architecture combining intra-piece and inter-piece structures, enabling the GNN to generalize across compositions while retaining individual stylistic identities. Experimental results on publicly available symbolic music datasets demonstrate that the proposed model outperforms traditional convolutional neural network (CNN) and recurrent neural network (RNN)-based models in classification accuracy and robustness across multiple musical genres. These findings highlight the potential of graph-based deep learning for extracting structural patterns critical to music understanding and classification.

Accurate modeling of T cell receptor (TCR)-peptide-major histocompatibility complex (pMHC) interactions is critical for understanding immune recognition. In this study, we present advances in structural modeling of TCR-pMHC class I complexes focusing on improving docking quality scoring and structural model selection using graph neural networks (GNN). We find that AlphaFold-Multimer's confidence score in certain cases correlates poorly with DockQ quality scores, leading to overestimation of model accuracy. Our proposed GNN solution achieves a 25% increase in Spearman's correlation between predicted quality and DockQ (from 0.681 to 0.855) and improves docking candidate ranking. Additionally, the GNN completely avoids selection of failed structures. Additionally, we assess the ability of our models to distinguish binding from non-binding TCR-pMHC interactions based on their predicted quality. Here, we demonstrate that our proposed model, particularly for high-quality structural models, is capable of discriminating between binding and non-binding complexes in a zero-shot setting. However, our findings also underlined that the structural pipeline struggled to generate sufficiently accurate TCR-pMHC models for reliable binding classification, highlighting the need for further improvements in modeling accuracy.

Democratizing artificial intelligence (AI) enables model development by clinicians with a lack of coding expertise, powerful computing resources, and large, well-labeled data sets. To determine whether resource-constrained clinicians can use self-training via automated machine learning (ML) and public data sets to design high-performing diabetic retinopathy classification models. This diagnostic quality improvement study was conducted from January 1, 2021, to December 31, 2021. A self-training method without coding was used on 2 public data sets with retinal images from patients in France (Messidor-2 [n = 1748]) and the UK and US (EyePACS [n = 58 689]) and externally validated on 1 data set with retinal images from patients of a private Egyptian medical retina clinic (Egypt [n = 210]). An AI model was trained to classify referable diabetic retinopathy as an exemplar use case. Messidor-2 images were assigned adjudicated labels available on Kaggle; 4 images were deemed ungradable and excluded, leaving 1744 images. A total of 300 images randomly selected from the EyePACS data set were independently relabeled by 3 blinded retina specialists using the International Classification of Diabetic Retinopathy protocol for diabetic retinopathy grade and diabetic macular edema presence; 19 images were deemed ungradable, leaving 281 images. Data analysis was performed from February 1 to February 28, 2021. Using public data sets, a teacher model was trained with labeled images using supervised learning. Next, the resulting predictions, termed pseudolabels, were used on an unlabeled public data set. Finally, a student model was trained with the existing labeled images and the additional pseudolabeled images. The analyzed metrics for the models included the area under the receiver operating characteristic curve (AUROC), accuracy, sensitivity, specificity, and F1 score. The Fisher exact test was performed, and 2-tailed P values were calculated for failure case analysis. For the internal validation data sets, AUROC values for performance ranged from 0.886 to 0.939 for the teacher model and from 0.916 to 0.951 for the student model. For external validation of automated ML model performance, AUROC values and accuracy were 0.964 and 93.3% for the teacher model, 0.950 and 96.7% for the student model, and 0.890 and 94.3% for the manually coded bespoke model, respectively. These findings suggest that self-training using automated ML is an effective method to increase both model performance and generalizability while decreasing the need for costly expert labeling. This approach advances the democratization of AI by enabling clinicians without coding expertise or access to large, well-labeled private data sets to develop their own AI models.

The process of extracting meaningful rules from big and complex data is called data mining. Data mining has an increasing popularity in every field today. Data units are established in customer-oriented industries such as marketing, finance and telecommunication to work on the customer churn and acquisition, in particular. Among the data mining methods, classification algorithms are used in studies conducted for customer acquisition to predict the potential customers of the company in question in the related industry. In this study, bank marketing data set in UCI Machine Learning Data Set was used by creating models with the same classification algorithms in different data mining programs. Accuracy, precision and f- measure criteria were used to test performances of the classification models. When creating the classification models, the test and training data sets were randomly divided by the holdout method to evaluate the performance of the data set. The data set was divided into training and test data sets with the 60-40%, 75­25% and 80-20% separation ratios. Data mining programs used for these processes are the R, Knime, RapidMiner and WEKA. And, classification algorithms commonly used in these platforms are the k-nearest neighbor (k-nn), Naive Bayes, and C4.5 decision tree.

Contact: Oliver.Serang@Childrens.Harvard.edu

Extracting planar roof structures from very high resolution images using graph neural networks

Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which completely gets rid of the rules defined by experts. However, due to the lack of useful prior knowledge, the prediction performance and interpretability of the GNNs may be affected. In this study, we introduced a new GNN model called RG-MPNN for chemical property prediction that integrated pharmacophore information hierarchically into message-passing neural network (MPNN) architecture, specifically, in the way of pharmacophore-based reduced-graph (RG) pooling. RG-MPNN absorbed not only the information of atoms and bonds from the atom-level message-passing phase, but also the information of pharmacophores from the RG-level message-passing phase. Our experimental results on eleven benchmark and ten kinase data sets showed that our model consistently matched or outperformed other existing GNN models. Furthermore, we demonstrated that applying pharmacophore-based RG pooling to MPNN architecture can generally help GNN models improve the predictive power. The cluster analysis of RG-MPNN representations and the importance analysis of pharmacophore nodes will help chemists gain insights for hit discovery and lead optimization.Graphical

Trainable-parameter-free structural-diversity message passing for graph neural networks.

Magnetic hyperthermia therapy (MHT) is an emerging noninvasive treatment for liver cancer that depends on accurate digital liver reconstruction. However, the limited availability of annotated medical data, particularly for tumors and vascular structures, hinders effective model training. This challenge is compounded by the need for individualized anatomical modeling in portable healthcare systems powered by consumer electronics. To overcome these challenges, we present a generative AI framework that integrates a conditional diffusion model with a graph neural network (GNN) to achieve high-fidelity, patient-specific liver reconstruction. Our framework combines a conditional diffusion model, which synthesizes realistic CT images to enrich liver anatomy, with a graph neural network that refines 3D surface reconstructions. Evaluated on public and clinical datasets, the method achieves higher segmentation accuracy and surface quality than existing approaches. By enhancing preoperative temperature-field simulations, the proposed approach supports individualized MHT planning and shows the potential of embedding generative AI in consumer healthcare devices.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

Cold-start problem is a fundamental challenge for recommendation tasks. Despite the recent advances on Graph Neural Networks (GNNs) incorporate the high-order collaborative signal to alleviate the problem, the embeddings of the cold-start users and items aren't explicitly optimized, and the cold-start neighbors are not dealt with during the graph convolution in GNNs. This paper proposes to pre-train a GNN model before applying it for recommendation. Unlike the goal of recommendation, the pre-training GNN simulates the cold-start scenarios from the users/items with sufficient interactions and takes the embedding reconstruction as the pretext task, such that it can directly improve the embedding quality and can be easily adapted to the new cold-start users/items. To further reduce the impact from the cold-start neighbors, we incorporate a self-attention-based meta aggregator to enhance the aggregation ability of each graph convolution step, and an adaptive neighbor sampler to select the effective neighbors according to the feedbacks from the pre-training GNN model. Experiments on three public recommendation datasets show the superiority of our pre-training GNN model against the original GNN models on user/item embedding inference and the recommendation task.

Technology CAD (TCAD) tools are pivotal for transistor modeling, enabling physics-based simulations with high accuracy essential for developing next-generation technology nodes. However, their high computational cost and low throughput severely constrain large-scale design-space exploration. Accelerating TCAD simulations while preserving physical accuracy has therefore become a central research objective. A promising approach involves the use of graph-based representations of TCAD meshes fed into graph neural networks (GNNs), which offer rapid and accurate alternatives. However, traditional GNNs, such as Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs) often falter with noisy transistor-mesh graphs, where embeddings become cluttered and complex. In thiswork, we propose a self-supervisedGNNframework for TCAD device modeling, specifically employing the Self-Supervised GAT (SuperGAT).We demonstrate that self-supervised GNN models, in contrast to GCNs and GATs, introduces a self-supervised edge-prediction task to distinguish informative from redundant topological connections. Our evaluation compares various GNN aggregation schemes demonstrates that SuperGAT successfully reproduces key transistor transfer characteristics drain current–gate voltage (Id–Vg) curves, subthreshold swing (SS) and threshold voltage (Vth). The dataset was generated using 14nm FDSOI transistors. Our SuperGAT model achieves a minimum <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.992 across all characteristics and an inference time of only 0.007 134 sec per transistor, resulting in a speed-up exceeding 100 000 x compared to TCAD simulation, which underscore the capability to substantially accelerate TCAD transistor simulations in advanced technology development.

Understanding customer behavior and predicting customer churn remain vital and central strategic objectives for modern digital organizations. Customer interactions are becoming multi-touch, they are cross-channel, and dynamically interconnected. The traditional linear modeling approach does not meet these demands. The traditional model does not capture the complexity that is vital in digital customer journeys. These challenges should be addressed, and graph machine learning (GraphML) and graph neural networks (GNNs) have become effective frameworks for addressing these challenges. The frameworks are capable of modeling the behavior of the customers as a network of entities, transitions, and interactions. The paper explores how graph machine learning can be applied to map the journeys of the customer and predict churn with higher accuracy, as well as deeper behavioral insight than conventional models. This paper will draw insights from peer-reviewed sources. It develops a conceptual, training graph model to detect churn signals, a multi-layered framework for constructing customer journey graphs, and operationalizing predictions into retention strategies that are actionable. The findings depict that graph-based modeling captures network dependencies. It captures cross-channel transitions and the behavior patterns that traditional machine learning does not incorporate. The findings indicate that GraphML offers a better method for describing customers as interconnected nodes within a relational structure. It enables more accurate churn prediction and a comprehensive mapping of the real-world customer journeys. The paper will conclude with a managerial implication, suggestions for future research, and limitations.