Omics data, such as transcriptomics, proteomics, and metabolomics, provide critical insights into disease mechanisms and clinical outcomes. However, their high dimensionality, small sample sizes, and intricate biological networks pose major challenges for reliable prediction and meaningful interpretation. Graph Neural Networks (GNNs) offer a promising way to integrate prior knowledge by encoding feature relationships as graphs. Yet, existing methods typically rely solely on either an externally curated feature graph or a data-driven generated one, which limits their ability to capture complementary information. To address this, we propose the external and generated Graph Neural Network (engGNN), a dual-graph framework that jointly leverages both external known biological networks and data-driven generated graphs. Specifically, engGNN constructs a biologically informed undirected feature graph from established network databases and complements it with a directed feature graph derived from tree-ensemble models. This dual-graph design produces more comprehensive embeddings, thereby improving predictive performance and interpretability. Through extensive simulations and real-world applications to gene expression data, engGNN consistently outperforms state-of-the-art baselines. Beyond classification, engGNN provides interpretable feature importance scores that facilitate biologically meaningful discoveries, such as pathway enrichment analysis. Taken together, these results highlight engGNN as a robust, flexible, and interpretable framework for disease classification and biomarker discovery in high-dimensional omics contexts.

Urban morphology has long been recognized as a factor shaping human mobility, yet comparative and formal classifications of urban form across metropolitan areas remain limited. Building on theoretical principles of urban structure and advances in unsupervised learning, we systematically classified the built environment of nine U.S. metropolitan areas using structural indicators such as density, connectivity, and spatial configuration. The resulting morphological types were linked to mobility patterns through descriptive statistics, marginal effects estimation, and post hoc statistical testing. Here we show that distinct urban forms are systematically associated with different mobility behaviors, such as reticular morphologies being linked to significantly higher public transport use (marginal effect = 0.49) and reduced car dependence (-0.41), while organic forms are associated with increased car usage (0.44), and substantial declines in public transport (-0.47) and active mobility (-0.30). These effects are statistically robust (p < 1e-19), highlighting that the spatial configuration of urban areas plays a fundamental role in shaping transportation choices. Our findings extend previous work by offering a reproducible framework for classifying urban form and demonstrate the added value of morphological analysis in comparative urban research. These results suggest that urban form should be treated as a key variable in mobility planning and provide empirical support for incorporating spatial typologies into sustainable urban policy design.

Precision rehabilitation aims to tailor movement training to improve outcomes. We tested whether proprioceptively-tailored robotic training improves hand function and neural processing in stroke survivors. Using a robotic finger exoskeleton, we tested two proprioceptively-tailored approaches: Propriopixel Training, which uses robot-facilitated, gamified movements to enhance proprioceptive processing, and Virtual Assistance Training, which reduces robotic aid to increase reliance on self-generated feedback. In a randomized controlled trial, forty-six chronic stroke survivors completed nine 2-hour sessions of Standard, Propriopixel or Virtual training. Among participants with proprioceptive deficits, Propriopixel ((Box and Block Test: 7 +/- 4.2, p=0.002) and Virtual Assistance (4.5 +/- 4.4 , p=0.068) yielded greater gains in hand function (Standard: 0.8 +/- 2.3 blocks). Proprioceptive gains correlated with improvements in hand function. Tailored training enhanced neural sensitivity to proprioceptive cues, evidenced by a novel EEG biomarker, the proprioceptive Contingent Negative Variation. These findings support proprioceptively-tailored training as a pathway to precision neurorehabilitation.

Integration and analysis of multi-omics data provide valuable insights for cancer subtype classification. However, such data are inherently heterogeneous, high-dimensional, and exhibit complex intra- and inter-modality dependencies. Recent advances in graph neural networks (GNNs) offer powerful tools for modeling such structure. Yet, most existing methods rely on prior knowledge or predefined similarity networks to construct graphs, which are often undirected or unweighted, failing to capture the directionality and strength of biological interactions. Interpretability at both the modality and feature levels also remains limited. To address these challenges, we propose TF-DWGNet, a novel Graph Neural Network framework that combines tree-based Directed Weighted graph construction with Tensor Fusion for multiclass cancer subtype classification. TF-DWGNet introduces two key innovations: a supervised tree-based approach for constructing directed, weighted graphs tailored to each omics modality, and a tensor fusion mechanism that captures unimodal, bimodal, and trimodal interactions using low-rank decomposition for efficiency. TF-DWGNet enables modality-specific representation learning, joint embedding fusion, and interpretable subtype prediction. Experiments on real-world cancer datasets show that TF-DWGNet consistently outperforms state-of-the-art baselines across multiple metrics and statistical tests. Moreover, it provides biologically meaningful insights by ranking influential features and modalities. These results highlight TF-DWGNet's potential for effective and interpretable multi-omics integration in cancer research.

Modern adaptive games require nuanced player understanding, yet most models use simplified 5-10 category taxonomies that fail to capture diversity. Behavioral clustering cannot distinguish players with different motivations who act similarly. We present a systematic evaluation of multi-modal classification at scale, combining behavioral telemetry with semantic context to support 36 player profiles. Using 19,413 gameplay sessions from an AI-controlled text-based RPG, we compared behavioral-only baselines with multi-modal approaches that integrate action sequences and semantic descriptions. Traditional clustering achieved only 10% accuracy for 36-category classification, limited by semantic conflation where opposite actions produced identical features. Our multi-modal LSTM processing action-text pairs improved accuracy to 21%, showing both potential and limits of non-conversational data. Analysis by behavioral complexity revealed that non-neutral profiles reached 42% accuracy (15x above random), while neutral profiles dropped to 25% (9x above random). Identical actions such as "help the merchant" cannot reveal whether a player is neutral or strategically waiting. Without access to reasoning, even multi-modal models struggle, though above-baseline results confirm a meaningful signal. Since prediction beyond 20 categories remains unexplored, our findings establish benchmarks for complex player modeling. Behavioral data alone plateaus near 10% for 36 categories, while multi-modal integration enables 25%. For designers, this shows that personality-based adaptation requires conversational interaction, as predefined choices cannot capture intent. Our evaluation at 36-category scale offers guidance for building adaptive games that better understand their players.

Integrating multi-omics data, such as DNA methylation, mRNA expression, and microRNA (miRNA) expression, offers a comprehensive view of the biological mechanisms underlying disease. However, the high dimensionality and complex interactions among omics layers present major challenges for predictive modeling. We propose Multi-Omics integration with Tree-generated Graph Neural Network (MOTGNN), a novel and interpretable framework for binary disease classification. MOTGNN employs eXtreme Gradient Boosting (XGBoost) to perform omics-specific supervised graph construction, followed by modality-specific Graph Neural Networks (GNNs) for hierarchical representation learning, and a deep feedforward network for cross-omics integration. On three real-world disease datasets, MOTGNN outperforms state-of-the-art baselines by 5-10% in accuracy, ROC-AUC, and F1-score, and remains robust to severe class imbalance (e.g., 87.2% vs. 33.4% F1 on imbalanced data). The model maintains computational efficiency through sparse graphs (2.1-2.8 edges per node) and provides built-in interpretability, revealing both top-ranked biomarkers and the relative contributions of each omics modality. These results highlight MOTGNN's potential to improve both predictive accuracy and interpretability in multi-omics disease modeling.

Life sciences research increasingly requires identifying, accessing, and effectively processing data from an ever-evolving array of information sources on the Linked Open Data (LOD) network. This dynamic landscape places a significant burden on researchers, as the quality of query responses depends heavily on the selection and semantic integration of data sources --processes that are often labor-intensive, error-prone, and costly. While the adoption of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles has aimed to address these challenges, barriers to efficient and accurate scientific data processing persist. In this paper, we introduce FAIRBridge, an experimental natural language-based query processing system designed to empower scientists to discover, access, and query biological databases, even when they are not FAIR-compliant. FAIRBridge harnesses the capabilities of AI to interpret query intents, map them to relevant databases described in scientific literature, and generate executable queries via intelligent resource access plans. The system also includes robust tools for mitigating low-quality query processing, ensuring high fidelity and responsiveness in the information delivered. FAIRBridge's autonomous query processing framework enables users to explore alternative data sources, make informed choices at every step, and leverage community-driven crowd curation when needed. By providing a user-friendly, automated hypothesis-testing platform in natural English, FAIRBridge significantly enhances the integration and processing of scientific data, offering researchers a powerful new tool for advancing their inquiries.

The integration of multi-omics data presents a major challenge in precision medicine, requiring advanced computational methods for accurate disease classification and biological interpretation. This study introduces the Multi-Omics Graph Kolmogorov-Arnold Network (MOGKAN), a deep learning model that integrates messenger RNA, micro RNA sequences, and DNA methylation data with Protein-Protein Interaction (PPI) networks for accurate and interpretable cancer classification across 31 cancer types. MOGKAN employs a hybrid approach combining differential expression with DESeq2, Linear Models for Microarray (LIMMA), and Least Absolute Shrinkage and Selection Operator (LASSO) regression to reduce multi-omics data dimensionality while preserving relevant biological features. The model architecture is based on the Kolmogorov-Arnold theorem principle, using trainable univariate functions to enhance interpretability and feature analysis. MOGKAN achieves classification accuracy of 96.28 percent and demonstrates low experimental variability with a standard deviation that is reduced by 1.58 to 7.30 percents compared to Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). The biomarkers identified by MOGKAN have been validated as cancer-related markers through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The proposed model presents an ability to uncover molecular oncogenesis mechanisms by detecting phosphoinositide-binding substances and regulating sphingolipid cellular processes. By integrating multi-omics data with graph-based deep learning, our proposed approach demonstrates superior predictive performance and interpretability that has the potential to enhance the translation of complex multi-omics data into clinically actionable cancer diagnostics.

For a brief moment in November, the TheoBros, a network of militant Christian nationalist influencers, made news when Trump nominated one of their allies, former Fox News commentator Pete Hegseth, to lead the Department of Defense. Hegseth attends a church that is affiliated with the TheoBro movement, and he has cited TheoBro patriarch Doug Wilson, a pastor in Moscow, Idaho, as someone who has had a major influence on him. While the controversies surrounding Hegseth's alleged alcohol abuse and mismanagement of funds meant for veterans continue to make the news, the TheoBros have receded into the background. But as it turns, out, they are embroiled in a major controversy of their own. A simmering divide over how Christians should regard Judaism has ignited into a conflagration.

Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration poses significant challenges due to the high dimensionality, data complexity, and distinct characteristics of various omics types. This study addresses these challenges and evaluates three graph neural network architectures for multi-omics (MO) integration based on graph-convolutional networks (GCN), graph-attention networks (GAT), and graph-transformer networks (GTN) for classifying 31 cancer types and normal tissues. To address the high-dimensionality of multi-omics data, we employed LASSO (Least Absolute Shrinkage and Selection Operator) regression for feature selection, leading to the creation of LASSO-MOGCN, LASSO-MOGAT, and LASSO-MOTGN models. Graph structures for the networks were constructed using gene correlation matrices and protein-protein interaction networks for multi-omics integration of messenger-RNA, micro-RNA, and DNA methylation data. Such data integration enables the networks to dynamically focus on important relationships between biological entities, improving both model performance and interpretability. Among the models, LASSO-MOGAT with a correlation-based graph structure achieved state-of-the-art accuracy (95.9%) and outperformed the LASSO-MOGCN and LASSO-MOTGN models in terms of precision, recall, and F1-score. Our findings demonstrate that integrating multi-omics data in graph-based architectures enhances cancer classification performance by uncovering distinct molecular patterns that contribute to a better understanding of cancer biology and potential biomarkers for disease progression.