Yuan Luo

Advances in scientific AI have accelerated hypothesis generation, prediction, and experimental design, but translating these capabilities into reliable physical experimentation remains a major bottleneck in scientific discovery. Although automation, high-throughput experimentation, foundation models, and cloud infrastructure have accelerated individual stages of the scientific workflow, they have not unified the discovery process. Scientists must still coordinate between computational models and physical laboratory systems to translate scientific designs into executable experiments and ensure their results are reliable. We argue that the next generation of laboratories will be agentic: environments in which scientists, AI systems, equipment and robotic platforms operate as collaborative discovery partners. We propose an architecture built around two complementary components: an agentic foundation that translates scientific designs into coordinated execution plans, and an evolving laboratory world model that represents physical state, predicts action outcomes, and incorporates experimental feedback. Together, they connect computational reasoning to observable and verifiable physical execution. Multimodal perception, robotics, and human–AI interfaces can make laboratory actions and evolving experimental conditions accessible to these systems, supporting adaptive execution and learning. We further propose an evaluation framework centered on physical grounding, execution fidelity, scientific validity, and adaptive coordination under human oversight, rather than autonomy alone. This Perspective outlines how agentic laboratories could connect scientific reasoning, physical experimentation, and trustworthy evidence within a human-in-the-lead discovery loop.

Background and ObjectivesMachine learning (ML) and natural language processing (NLP) approaches are increasingly used to support nuanced phenotyping, surveillance, and trial readiness using electro...

Polygenic risk scores are widely used for predicting genetic risk across complex diseases and traits, and several pre-trained models have been developed. Few approaches leverage these pre-trained polygenic risk scores to further refine predictive performance. Here, we present Adaptive Boosting of pre-trained Polygenic Risk Socres, a fine-tuning framework that refines pre-trained polygenic risk score models through adaptive variable selection and model boosting to identify additional predictive signals that may not be fully captured by the original models. Simulations show that our framework can identify signals orthogonal to pre-trained polygenic risk scores while controlling false discovery rates. Using UK Biobank data, we fine-tune pre-trained polygenic risk scores for binary diseases and continuous traits, and validate the results across three independent datasets: All of Us, eMERGE, and Penn Medicine Biobank. Real data analyses show that Adaptive Boosting of pre-trained Polygenic Risk Scores achieves statistically significant improvements in several scenarios while maintaining competitive performance in others.

Background: The current landscape of emergency care (EC) is marked by high demand, leading to issues such as emergency department boarding, overcrowding, and subsequent delays that impact the quality and safety of patient care. Integrating data science into EC can enhance decision-making with predictive, preventative, personalized, and participatory approaches. However, gaps in adherence to fairness, accountability, interpretability, and responsibility are evident, particularly due to barriers to data-sharing, which often result in a lack of transparency and robust oversight in these applications. Objective: The FAIR-EC (Fair, Accountable, Interpretable, and Responsible-Emergency Care) collaboration adapts the existing Fair, Accountable, Interpretable, and Responsible principles to address emerging challenges as data science integrates with EC. This initiative aims to transform EC by establishing ethical artificial intelligence standards specifically tailored for this integration. By bridging the gap between EC professionals, data scientists, and other stakeholders, the collaboration promotes international cooperation that leverages advanced data science techniques to enhance EC outcomes across different care settings. Methods: We propose a federated research design to analyze extensive datasets from various global institutions without compromising patient privacy. This approach transforms epidemiological research with advanced data science techniques, emphasizing the harmonization of data for comprehensive analyses across different health care systems. Results: The FAIR-EC initiative has facilitated the identification and harmonization of datasets from diverse geographical regions, enabling the examination of regional variations in EC practices. As of paper submission, participating sites have identified retrospective EC datasets totaling >2 million records (eg, Duke Health >400,000 and Singapore General Hospital >1.7 million records). Initial projects have demonstrated feasibility and operational readiness, including implementation of federated workflows and ongoing development of a federated scoring system, cross-site evaluation, and adaptation of association studies and predictive models across various regions. Cross-site harmonization and pilot analyses are underway (with local ethics approvals in progress), and first multisite results are expected to be submitted in mid-late 2026, with additional project-level publications anticipated in 2027. These efforts highlight the feasibility of leveraging advanced data science techniques to address the complexities of EC while preserving patient privacy without centralizing individual-level data. This project was funded from September 1, 2022, to August 31, 2023. Conclusions: FAIR-EC integrates data science ethically and effectively into EC, addressing challenges such as fragmented data, real-time handoffs, and public health crises. Its federated design harmonizes diverse data streams while preserving privacy, and its emphasis on ethical artificial intelligence aligns with the dynamic nature of EC. Despite challenges in data variability and system complexity, FAIR-EC establishes a strong foundation for innovation in global EC.

Single-cell transcriptomics resolves CAR T-cell states, yet translating heterogeneous cellular signals into patient-level therapeutic response remains challenging. Existing studies primarily identify response-associated genes or cell populations through experimental and statistical analyses, but few predictive frameworks integrate gene-level structure with clinical outcomes. Here, we present gANCHOR, a T-cell foundation model built on a hierarchical hypergraph attention framework combining biologically informed representation learning with patient-level response prediction. By encoding gene-pathway relationships, gANCHOR learns pathway-aware cell embeddings that improve biological conservation and batch robustness. A cell-to-patient attention module then aggregates cellular information to infer therapeutic response. Across benchmark datasets, gANCHOR achieved the strongest overall performance in biological conservation and batch-correction assessments. In response prediction across 161 patients from five CAR T-cell studies, gANCHOR achieved an F1 score of 0.87, outperforming benchmarked single-cell foundation models. gANCHOR also identified reproducible response- and non-response-associated gene programs, providing interpretable biological insights into CAR T-cell efficacy and resistance.

Advances in imaging- and sequencing-based spatial transcriptomics have increased molecular throughput and resolution, enabling the measurement and analysis of spatial transcriptomes at single-cell resolution. However, accurate cell segmentation remains challenging because cell morphology, tissue processing and staining methods vary across samples and platforms, limiting the accuracy and generalizability of existing algorithms. Here we show that DISSECT, a cell segmentation model integrating cytological images with spatial transcriptomic profiles, improves spatial single-cell transcriptome reconstruction. DISSECT uses a pretrained deep generative model to denoise multiscale image features, predicts cell instances with an instance-aware detection module and applies image- and transcriptome-derived gradient fields to refine segmentation masks. Benchmarking across multiple datasets showed that DISSECT achieved higher mean average precision than several existing segmentation tools. We further applied DISSECT to three pairs of gastric adenocarcinoma samples collected before and after anti-PD-1 treatment and profiled by Stereo-seq, illustrating its utility for downstream spatial biological interpretation.