Singapore

The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome this challenge we propose STProtein, a novel framework leveraging graph neural networks with multi-task learning strategy. STProtein is designed to accurately predict unknown spatial protein expression using more accessible spatial multi-omics data, such as spatial transcriptomics. We believe that STProtein can effectively addresses the scarcity of spatial proteomics, accelerating the integration of spatial multi-omics and potentially catalyzing transformative breakthroughs in life sciences. This tool enables scientists to accelerate discovery by identifying complex and previously hidden spatial patterns of proteins within tissues, uncovering novel relationships between different marker genes, and exploring the biological &quot;Dark Matter&quot;. 


AAAI 2026

STProtein: predicting spatial protein expression from multi-omics data

spatial omics

scientific discovery

dark matter

graph neural network

We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The purpose of the AAAI conference series is to promote research in Artificial Intelligence (AI) and foster scientific exchange between researchers, practitioners, scientists, students, and engineers across the entirety of AI and its affiliated disciplines. AAAI-26 will feature technical paper presentations, special tracks, invited speakers, workshops, tutorials, poster sessions, senior member presentations, competitions, and exhibit programs, and a range of other activities to be announced.<br><br>

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

The integration of spatial multi-omics data from single tissues is crucial for advancing biological research. However, a significant data imbalance impedes progress: while spatial transcriptomics data is relatively abundant, spatial proteomics data remains scarce due to technical limitations and high costs. To overcome this challenge we propose STProtein, a novel framework leveraging graph neural networks with multi-task learning strategy. STProtein is designed to accurately predict unknown spatial protein expression using more accessible spatial multi-omics data, such as spatial transcriptomics. We believe that STProtein can effectively addresses the scarcity of spatial proteomics, accelerating the integration of spatial multi-omics and potentially catalyzing transformative breakthroughs in life sciences. This tool enables scientists to accelerate discovery by identifying complex and previously hidden spatial patterns of proteins within tissues, uncovering novel relationships between different marker genes, and exploring the biological "Dark Matter". 


workshop paper

Spatial transcriptomics is an emerging technology to study gene expression and cell type interactions at spatial resolution. To assess which spatial transcriptomics platform is more suitable for studying brain samples, we performed a comparison between Visium HD and Xenium (including 5k and brain panel) on a human hypothalamus sample. Xenium brain panel and 5k datasets showed better sensitivity for transcript detection compared to Visium HD, similar to the observations from other studies. Xenium data also showed higher expression level for cell type markers and key genes involved in appetite regulation in the hypothalamus. In addition, we observed strong correlation in cell type expression between the Xenium brain panel and 5k data. Further benchmarking against relevant snRNA-seq data showed moderate correlation for cell type expression. Surprisingly, we found comparable specificity for known cell type markers for both neuronal and non-neuronal cell types relative to snRNA-seq data, albeit substantial challenges in cell segmentation based on Xenium data. However, discrepancies were observed between cell type label transfer from relevant snRNA-seq data and manual annotation.\n\nIn a pilot study, we utilized Xenium platform to profile spatial transcriptomics on mouse hypothalamus and hindbrain, in exploration of sex and age associated gene expression differences. In total 247 genes were assayed from the 10x brain panel, supplemented by 100 custom genes including known cell type markers and genes of interest. We applied Cellpose on 18s rRNA and DAPI staining for cell segmentation, which yielded 80,000 and 200,000 cells from hindbrain and hypothalamus, respectively, across 8 mice after quality control. With scRNA-seq approaches for downstream clustering and annotation, we were able to identify major cell types including neurons, glial cells and vascular cells. Cell type compositional and neighbourhood analysis highlighted differences in cell type abundance and neighbourhood enrichment between young mice and old mice. Differential gene expression analysis uncovered more pronounced differences associated with age as opposed to sex. In summary, our analysis demonstrated that spatial transcriptomics is a promising tool to decipher biology in the brain. 


Explorations of spatial transcriptomics on brain samples for biological understanding

Understanding how gene expression evolves over time after trauma is central to modeling immune responses, yet single-cell temporal data remain sparse and heterogeneous across cell types. Using a temporal trauma scRNA-seq dataset, we formulate the task of predicting next-time gene expression from earlier observations under a cross-cell-type generalization setting. \nWe introduce the Dynamic Consistency Index (DCI), which quantifies how consistently a gene’s temporal trajectory aligns across cell types, serving as a measure of biological regularity and predictability. High-DCI genes exhibit reproducible temporal dynamics and are markedly easier to model. \nBy integrating DCI-based gene selection with a recurrent neural architecture trained under a Gaussian negative log-likelihood objective, we achieve superior accuracy and well-calibrated uncertainty compared to deterministic baselines. \nOverall, DCI reliably identifies dynamically consistent genes, and uncertainty-aware recurrent modeling provides a robust framework for capturing cross-cell-type gene-expression evolution. 



Dynamic Consistency Reveals Predictable Genes in Cross-Cell Type Temporal scRNA-Seq Data

The integration of Large Language Models into high-stakes clinical workflows is critically hampered by their lack of verifiable reliability and tendency to generate hallucinations. This paper introduces Med-ICE, an autonomous framework designed to enhance the reliability of LLMs for medical applications. Med-ICE adapts the Iterative Consensus Ensemble paradigm, enabling a group of peer LLM agents to collaboratively converge on a final answer through iterative rounds of generation and peer review, thereby eliminating the need for an external arbiter and its associated scalability bottleneck. Our work makes three key contributions: (1) a novel semantic consensus mechanism that determines agreement based on semantic similarity, crucial for nuanced clinical language; (2) demonstration of state-of-the-art performance, where Med-ICE significantly outperforms both direct single-LLM generation and the Self-Refinement technique on challenging medical benchmarks; and (3) a highly efficient and scalable architecture, as our Semantic Consensus Monitor is computationally lightweight. This research establishes a new standard for developing safer, more trustworthy LLM systems, paving the way for their responsible integration into medicine. 


Med-ICE: Enhancing Factual Accuracy in Medical AI through Autonomous Multi-Agent Consensus

Transfer learning across domains with mismatched and non-mappable feature spaces is a fundamental challenge in machine learning. Existing methods often rely on brittle feature-mapping or risk catastrophic forgetting during fine-tuning. To address this, we introduce the Cross-Species Latent Alignment Network (CSLAN), a novel framework for robust knowledge transfer. CSLAN pioneers a three-pronged approach: (1) we employ a sparse regression model for principled selection of informative features in each domain, reducing noise and dimensionality. (2) We pre-train an encoder-decoder on a comprehensive source domain (mouse). (3) We introduce a biologically-inspired asymmetric fine-tuning strategy, where the pre-trained decoder and latent processor—encapsulating conserved class definitions—are frozen. A new target-specific encoder (for human) is then trained from scratch to project its distinct feature space into this preserved, semantically structured latent space. On a challenging cross-species trauma-related single-cell classification task, CSLAN achieved 95.83% accuracy using only a few hundred labeled human cells, significantly outperforming standard baselines. Our work establishes a powerful paradigm for aligning mismatched domains, demonstrating that decoupling feature projection from a conserved decision manifold is key to effective transfer. 


CSLAN: Cross-Species Latent Alignment Network for Trauma-Related Cell-Type Classification

Urban land use maps represent a fundamental data resource in geographic information science, urban planning, and urban governance, essential for urban development and management. However, due to the complexity of urban morphology and pronounced spatiotemporal heterogeneity, the generation of real-time, high precision land use maps remains heavily reliant on manual annotation by professional surveying agencies, leading to inefficiency and high costs. We propose POI Conditioned Hybrid Latent Diffusion–GAN (PH-LDM-GAN), a GIS integrated, end-to-end framework that generates land use maps directly from open source POI data. The framework integrates the Latent Diffusion Model (LDM), incorporates a lightweight GAN, and leverages R3GAN’s adversarial regularization concept, effectively reducing the need for manual annotation and land use delineation to just a few hours. Experimental results demonstrate significant improvements across all evaluation metrics (FID, LPIPS, and mIoU) compared with the baseline latent diffusion model, indicating superior semantic and structural consistency. Furthermore, the framework has been integrated into a GIS-based mapping tool, providing a scalable and practical solution for urban planning and geospatial intelligence applications.

A GIS-Oriented land use Map Generation Agent: A Hybrid Approach Integrating Diffusion Models and GANs

In this paper, we study the vehicle routing problem with a finite time horizon. In this routing problem, the objective is to maximize the number of customer requests served within a finite time horizon. We present a novel routing network embedding module which creates local node embedding vectors and a context-aware global graph representation. The proposed Markov decision process for the vehicle routing problem incorporates the node features, the network adjacency matrix and the edge features as components of the state space. We incorporate the remaining finite time horizon into the network embedding module to provide a proper routing context to the embedding module. We integrate our embedding module with a policy gradient-based deep Reinforcement Learning framework to solve the vehicle routing problem with finite time horizon. We trained and validated our proposed routing method on real-world routing networks, as well as synthetically generated Euclidean networks. Our experimental results show that our method achieves a higher customer service rate than the existing routing methods. Additionally, the solution time of our method is significantly lower than that of the existing methods.

Vehicle Routing with Finite Time Horizon using Deep Reinforcement Learning with Improved Network Embedding

Urban development plays a crucial role in promoting environmental sustainability. Most existing AI-driven urban studies focus on using satellite imagery and deep learning for monitoring and understanding past urban development, whereas urban development is inherently a forward-looking, generative process aimed at envisioning future cities. We present UrbanControlNet, a multimodal generative AI framework that reimagines global urban development through high-fidelity and controllable satellite imagery generation. By integrating digital elevation models, road networks, and urban density metric conditioning, UrbanControlNet generates realistic and counterfactual urban imagery. Trained on millions of samples from 500 global cities, UrbanControlNet supports visual cross-city transfer, scenario exploration, and generative augmentation for downstream tasks (e.g., global fossil fuel carbon emission prediction). The framework bridges urban analytics and generative design, offering a powerful tool and assistant for global urban planning. The codes and models: https://github.com/kailaisun/UrbanControlNet.

UrbanControlNet: Reimaging Global Urban Development with Satellite Imagery

Computer graphics, comprising both raster and vector components, is a fundamental part of modern science, industry, and digital communication. While raster graphics offer ease of use, their pixel-based structure limits scalability. Vector graphics, defined by mathematical primitives, provide scalability without quality loss, it is more complex to produce. For design and architecture, the versatility of vector graphics is paramount, despite its computational demands. This paper introduces a novel method for generating vector residential plans from the textual descriptions. Our approach surpasses existing solutions by approximately 5% in CLIPScore-based visual quality, benefiting from its inherent handling of right angles and flexible settings. Additionally, we present a new algorithm for vectorizing raster plans into structured vector images.

Text-to-Vector Conversion for Residential Floor Plan Design

The widespread deployment of electric vehicles (EVs) has made charging demand prediction crucial for urban resource allocation. However, existing studies primarily focus on improving overall predictive accuracy while overlooking prediction fairness. More importantly, explanation fairness has been overlooked across the broader traffic forecasting literature, despite its critical role in ensuring transparency and accountability in public resource allocation. To address these limitations, we introduce two fairness metrics that evaluate regional disparities in prediction errors and explanation quality. We then develop $DualFairNet$, a general fairness-enhancing framework compatible with various spatiotemporal graph neural networks $(ST\text{-}GNNs)$. The framework includes an adaptive attention module jointly guided by prediction error and explanation quality, which dynamically adjusts the graph structure to emphasize regions with poor prediction accuracy or weak explanation quality, and a multi-objective loss function with dual fairness regularization terms, enabling simultaneous optimization of accuracy, result fairness, and explanation fairness. Experiments on a real-world dataset demonstrate that $DualFairNet$ substantially improves fairness—reducing prediction error and explanation quality disparities by 20.18% and 27.37% respectively—with negligible impact on accuracy (e.g., 0.16% increase in Mean Absolute Percentage Error). $DualFairNet$ contributes to building fair intelligent charging systems and serves as a general framework for multiple $ST\text{-}GNN$ models.

DualFairNet: Enhancing Prediction and Explanation Fairness in Spatiotemporal Graphs With Electric Vehicle Charging Demand as a Case Study

Text-to-image diffusion models help visualize urban futures but can amplify group-level harms. We propose collective recourse: structured community "visual bug reports" that trigger fixes to models and planning workflows. We (1) formalize collective recourse and a practical pipeline (report, triage, fix, verify, closure); (2) situate four recourse primitives—counter-prompts, negative prompts, dataset edits, reward-model tweaks—within the diffusion stack; (3) define mandate thresholds via a mandate score combining severity, volume saturation, representativeness, and evidence; and (4) evaluate a synthetic program of 240 reports. Prompt-level fixes were fastest (median $2.1$-$3.4$ days) but less durable (21-38\% recurrence); dataset edits and reward tweaks were slower (13.5 and 21.9 days) yet more durable (12-18\% recurrence) with higher planner uptake (30-36\%). A threshold of $0.12$ yielded 93\% precision and 75\% recall; increasing representativeness raised recall to 81\% with little precision loss. We discuss integration with participatory governance, risks (overfitting to vocal groups), and safeguards (dashboards, rotating juries).

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