Results 121 to 130 of about 425,037 (266)
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang +3 more
wiley +1 more source
Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang +9 more
wiley +1 more source
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Droplet‐based microfluidics enables precise, high‐throughput microscale reactions but continues to face challenges in scalability, reproducibility, and data complexity. This review examines how artificial intelligence enhances droplet generation, detection, sorting, and adaptive control and discusses emerging opportunities for clinical and industrial ...
Junyan Lai +10 more
wiley +1 more source
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He +2 more
wiley +1 more source
MusicSwarm: Biologically Inspired Intelligence for Music Composition
Biologically inspired swarms of frozen foundation models self‐organize to compose complex music without fine‐tuning. By coordinating through stigmergic signals, decentralized agents dynamically evolve specialized roles and adapt to solve complex tasks.
Markus J. Buehler
wiley +1 more source
Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella +5 more
wiley +1 more source
By employing dimensionally reduced reaction descriptors, a human–machine collaboration framework for efficient electrochemical nitrate reduction to NH3 electrocatalysts screening is established and drastically shorten the discovery timeframe. A new kinetic model is established in combination with a rotating ring‐disk electrode, unveiling the pivotal ...
Yingying Cheng +3 more
wiley +2 more sources
Towards Advanced Intelligent and Perceptive Soft Grippers
Implementing soft yet strong and intelligent soft grippers request innovative and creative solutions in designing soft bodies and seamlessly integrating actuated systems with hierarchical sensing. This review systematically analyses soft grippers with a deep understanding of core components, from fundamental design principles to actuation and sensing ...
Haneul Kim +4 more
wiley +1 more source

