Abstract Predicting future vegetation patterns from climate change projections is central to assessing ecological impacts, but most existing climate–vegetation models predict coarse biome classes rather than fine‐grained land cover. Here we develop a constrained regression approach that predicts local land cover composition (LLCC)—the fractional cover ...
T. F. Stepinski
wiley +1 more source
Integrating miRNA profiling and machine learning for improved thyroid cancer diagnosis. [PDF]
Xu JY, Zhu XL, Hou L, Jiang J.
europepmc +1 more source
IGyTheM: Integrating Geodynamics With Thermodynamics Using Machine Learning
Abstract Achieving a comprehensive understanding of mantle convection requires integrated models that couple mantle dynamics with thermodynamic properties. Although advances in computational geodynamics have substantially improved our ability to simulate the underlying physical processes, incorporating thermodynamics into self‐consistent geodynamic ...
Qian Yuan +4 more
wiley +1 more source
Adapting a Pretrained LLM to Rapidly Recover Earthquake Magnitude and Location
Abstract Determining earthquake magnitude and location through fast, automated methods is fundamental for seismic monitoring. Recent studies have shown that Large Language Models (LLMs) can operate as pattern‐recognition systems capable of handling novel domains and purely numerical tasks.
Aurora Bassani +6 more
wiley +1 more source
Spatially multimodal and multiscale network for representation learning from spatial multi-omics. [PDF]
Zhang F, Peng C.
europepmc +1 more source
Stepwise multi-scale reconstruction of cell spatial organization from single-cell RNA sequencing data with Cell2space. [PDF]
Pan J, Guan Q, Sun D.
europepmc +1 more source
Large language models enhance annotation of enzymes in metagenomes. [PDF]
Zheng L, Li B, Xu S, Chen J, Liang G.
europepmc +1 more source
A Practical Workflow for Correcting Kit-Specific Effects in Whole-Exome Sequencing Data. [PDF]
Jarosz L +4 more
europepmc +1 more source
Dental age estimation in forensic odontology: a systematic review comparing traditional methods and artificial intelligence approaches. [PDF]
Lakhaouaja F, García Navarro A.
europepmc +1 more source

