10 papers · ranked by Valyu relevance
Kaicheng Shen, Weiyi Wang, Yang Wang, Yiqiang Wu + 3 more
Exposomics provides a systems-level framework to characterize the environmental exposures experienced across the life course and their biological consequences, offering critical insights into tumor initiation and precision prevention. Advances in sensing technologies, intelligent materials, and data science now enable…
Zichao Jin, Jiaoru Wang, Wenjiang Huang, Jingcheng Zhang + 2 more
Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework.…
Priyanka Bhutada, Nitin Goyal, Tatsam K. Lakhankiya, Sai D. Narahari + 8 more
Artificial Intelligence (AI) frameworks for automating scientific research have shown strong performance on benchmarks, but their utility for real-world industrial research remains insufficiently characterized. Extending the analysis presented in the first paper of this series, we evaluated the same five advanced AI…
Panagiotis Douris, George Kodovazenitis
The integration of artificial intelligence (AI) into dentistry is reshaping clinical workflows, opening new possibilities for population-level public health monitoring. Machine learning approaches, such as convolutional neural networks and other deep learning architectures, are becoming more and more capable to exhibit…
Yilan Wu, Dun Jack Fu, Yukun Zhou, Siegfried K Wagner + 2 more
Background Large language model (LLM) agents capable of generating and executing statistical code from natural language may broaden access to clinical data analysis, yet which pipeline stages they perform reliably and which require expert oversight remain poorly defined. Objective This study aimed to evaluate the…
Dianzhuo Wang, Qian Xu, Arjun Banerjee, Rishi Jain + 7 more
Inferring biological function from experimental data is central to understanding emerging pathogens and developing effective countermeasures, yet interpreting these data remains slow and expert-intensive. AI agents could help accelerate this process by reasoning across sequence, structural, and biophysical evidence. We…
Lysanne Veerle Michels, Lucy Smith, Suzan Ghannam, Charles Gadd + 1 more
Introduction Artificial intelligence (AI) has rapidly advanced as a key analytical tool for processing complex datasets across disciplines, including environmental and health research. Meteorological data is increasingly used to understand and mitigate health risks, often linked to climate change. This rapid review…
Yi Ren, Zimei Chen, Hayden Deans, Ying Nian Wu + 1 more
Extensive studies suggest the brain performs Bayesian inference to infer the latent world states. It is a fundamental neuroscience question that how canonical recurrent neural circuits in the brain implement Bayesian inference. Many existing theoretical studies focused on how the recurrent circuits compute the…
Amélie Barozet, Vincent Cabeli, Jean Ogier du Terrail, Alexey Rukhovich + 6 more
The development of climate-resilient crops would be greatly accelerated by models able to reason directly over plant genomic sequences and to pinpoint trait-associated regions or loci. Anticipating the impact of DNA base changes (variants) remains challenging, and understanding regulatory mechanisms is still an active…
Maria Juliana Rodriguez-Cubillos, Tomasz Zieliński, Jason R. Swedlow, T. Ian Simpson + 1 more
Ensuring the availability and accessibility of research data is fundamental to advancing knowledge, as codified in the FAIR principles (Findable, Accessible, Interoperable, and Reusable). Accurate metadata documentation is indispensable for meeting these principles; however, entries in deposition databases often…