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01Science · Science Current
Artificial intelligence (AI) in radiology aspires to deliver expert-level diagnosis across diverse clinical tasks, yet existing supervised strategies remain limited in scope. We developed RADAR, a generalist vision-language model trained on more than 400,000 contrast-enhanced abdominal computed tomography (CT) examinations and 15 million anatomy-wise image-text pairs, learning directly from clinical reports without manual annotation. Throughout internal and external evaluations across multiple centers and varied clinical scenarios, RADAR achieved high diagnostic performance and robust generalization for 18 anatomical structures and 146 imaging findings. In a reader study, RADAR assistance increased the diagnostic sensitivity of 26 radiologists by ~10%. RADAR offers a scalable, versatile, and interpretable solution for abdominal CT, demonstrating that generalist AI can match human experts in general and complicated radiology tasks.
Cell Press · Cell
scBaseCount is presently the largest public single-cell RNA-seq repository, containing over 502 million cells across 27 organisms and 75 tissues. An AI agent autonomously discovers, annotates, and uniformly reprocesses all 10× Genomics datasets in the SRA, creating a harmonized, continually updated resource for studying the diversity of cell biology and training AI models.
Nature Portfolio · Nature
Understanding why people age — and how to slow that process down — is an obsession that has stretched across millennia, from mythic quests for immortality to modern science’s hunt for drugs that lengthen lifespan. Now, that quest has entered the AI era. A report1 published today in Cell unveils a bespoke artificial-intelligence system that aims to turbocharge longevity research. The system introduces large language models (LLMs) that the authors trained on ageing-biology data. It also includes a suite of 17 tasks that can serve as benchmarks to gauge how well these and other LLMs perform on ageing-related projects. Finally, it includes an interface that brings the models and other ageing-related research tools together with AI assistants called agents to aid analyses. Gene clock predicts time to death in humans — and assesses ‘biological’ age The authors used their benchmarks to evaluate both their specialist LLMs and much larger cutting-edge commercial models produced by companies including OpenAI and DeepSeek, that are trained on larger, more diverse data sets to handle a broad range of tasks. In many — although not all — of the tests, the LLMs tailored to ageing research outperformed the large LLMs. “The paper makes a very important contribution to the longevity and ageing field,” says Marinka Zitnik, a computer scientist at Harvard Medical School in Boston, Massachusetts, who was not involved in the work. “It can inform the development of next-generation AI models.” The work also tackles a key question for the field: how can researchers train AI tools to understand ageing when scientists have not yet defined the concept for themselves? “For certain disease areas, such as cancer, the AI tasks can be very crisply defined,” says Zitnik. “Ageing is a very different beast. It is hard to define.” Clock collectors Researchers have spent decades gathering data to define that beast. One result has been a slew of ‘clocks’ aimed at measuring biological ageing, or the rate at which a person’s body exhibits signs of ageing. Such clocks measure biological age by assessing characteristics such as facial features, protein levels, gene activity and brain scans, all with the goal of quantifying whether the effects of ageing are outpacing or lagging behind someone’s chronological age in years. How quickly are you ageing? What molecular ‘clocks’ can tell you about your hea…
Nature Portfolio · Nature Computational Science
Abstract Molecular descriptors play a crucial role in representing the structural features of molecules for machine learning-based physical property prediction. However, current descriptors either consider only local aspects of molecular structures or fail to effectively learn nonlocal structural features involving long-distance intramolecular interactions. Here, to address this issue, we present a descriptor named TDiMS. TDiMS effectively summarizes the enumerated pairwise topological distances between molecular substructures, thus capturing nonlocal interactions. Our evaluation shows that TDiMS successfully identifies essential features of large structures and outperforms other representative descriptors in predicting properties for which distances between substructures are a primary factor. In addition, these identified features are highly interpretable for experts in materials discovery.
Hugging Face Daily Papers
JEPA-Anything: Learning Predictive Models across Different Worlds 摘要 World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization (OPF). Extending joint-embedding predictive architectures, OPF decomposes latent targets into complementary factors, learns them through dedicated pathways, and recombines them within a shared predictive design. We evaluate JEPA-Anything across seven domains: vision, biology, clinical trajectories, control, molecular dynamics, physical fields, and weather. Experiments span representation learning, intervention prediction, out-of-distribution generalization, and long-horizon dynamics, including 10 matched dynamics tasks, forecasting of over 1,000 clinical events, and 100-step molecular rollouts across four systems. Against matched JEPA baselines, JEPA-Anything improves reported metrics on all 10 dynamics tasks and reduces single-intervention prediction error on Interventional Pong by 34.8%. It achieves the lowest one-step and 100-step molecular errors among compared methods in all four systems. Beyond prediction, a factor-nominated biological intervention receives experimental support in cell co-cultures, patient-derived organoids, tumor fragments, and mice; latent orbital modes recover the Keplerian scaling exponent with a fitted slope of −1.4991. These results support a common factorized predictive principle across heterogeneous worlds, connecting world modeling with intervention and experimentally grounded scientific discovery. 作者 期刊/会议 引用 BibTeX @article{cui2026jepa, title={JEPA-Anything: Learning Predictive Models across Different Worlds}, author={Cui, Taoyong and Wang, Zhongyao and Xu, Xinyue and Liu, Weiyang and Yu, Zhaochen and Zhang, Yuying and Gao, Qiang and Yang, Mengyue and Ouyang, Wanli and Heng, Pheng Ann and Wu, Yingcheng and Yin, Zhenfei and Yang, Ling}, journal={arXiv preprint arXiv:2609.20800}, year={2026} }
