Results 151 to 160 of about 5,814,361 (281)

Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art

open access: yesAdvanced Intelligent Discovery, EarlyView.
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser   +6 more
wiley   +1 more source

Autonomous AI‐Driven Design for Skin Product Formulations

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang   +5 more
wiley   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

open access: yesAdvanced Intelligent Discovery, EarlyView.
Artificial intelligence, and foundation models in particular, are transforming life sciences and medicine. This perspective reviews biological and medical foundation models across scales, highlighting key challenges in data availability, model evaluation, and architectural design.
Kunying Niu   +3 more
wiley   +1 more source

Art therapy and emotional pain: a scoping review of physiological and biological measures. [PDF]

open access: yesFront Hum Neurosci
Moezzi S   +4 more
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
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

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

open access: yesAdvanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha   +2 more
wiley   +1 more source

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