Results 161 to 170 of about 1,037,857 (302)

Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics

Advanced Intelligent Discovery, EarlyView.
Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

When Biology Meets Medicine: A Perspective on Foundation Models

Advanced 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, Yuxing Wang, Limin Zou, Jing Huang   +3 more
wiley   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

In Situ Contact Angle Measurement for Autonomous Spin Coating in Self‐Driving Labs

Advanced Intelligent Discovery, EarlyView.
A vision‐based add‐on transforms commercial spin coaters into autonomous modules of Self‐Driving Labs. Combining a width‐scaled U‐Net with classical geometric analysis, the system simultaneously measures contact angles and estimates substrate pose using a single camera.
Sven Fischer, Micha Hiegle, Holger Röhm
wiley   +1 more source

Semantic Segmentation with Reverse Attention [PDF]

Procedings of the British Machine Vision Conference 2017, 2017
Qin Huang 0006, Chi-Hao Wu 0001, Chunyang Xia, Ye Wang 0013, C.-C. Jay Kuo   +4 more
openaire   +2 more sources

Generic Semantic Segmentation of Historical Maps [PDF]

Research in automatic map processing is largely focused on homogeneous corpora or even individual maps, leading to inflexible models. Based on two new corpora, the first one centered on maps of Paris and the second one gathering maps of cities from all ...
Kaplan, Frédéric, Di Lenardo, Isabella, Petitpierre, Rémi Guillaume   +2 more
core  

Artificial Intelligence‐Driven Network Pharmacology: A Methodological Paradigm Shift Bridging Traditional Wisdom and Modern Science

Advanced Intelligent Discovery, EarlyView.
Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang, Sile Hu, Rigahai Ji, Riguna A, Siqige Hao, Ma Zhuo, Yi‐Hung Chen, Bisihalatu Hu, Ulana Bai, Xigui Hada   +9 more
wiley   +1 more source

Accelerating the Discovery of Proton Conducting Electrolytes via Machine Learning‐Enabled Literature Mining

Advanced Intelligent Discovery, EarlyView.
An end‐to‐end knowledge discovery framework is established to automate high‐precision property extraction from small, specialized literature corpora. Utilizing a domain‐specific bidirectional encoder representation from a transformer model and data augmentation, the system accurately extracts and structures electrolyte performance data, ultimately ...
Gaheun Shin, Seongho Lee, Seulchan Kim, Ho‐Il Ji, Joonhee Kang   +4 more
wiley   +1 more source

U‐Net‐Based Prediction of Concealed Volume Loss in AZ31 From Optical Corrosion Imprints and As‐Exposed Surface Topography

Advanced Intelligent Discovery, EarlyView.
How much material loss is concealed beneath a layer of corrosion products? This proof‐of‐concept study demonstrates how concealed volume loss is inferred from the optical appearance and as‐exposed surface topography of AZ31 corrosion imprints. By predicting postcleaning depth maps, a multimodal U‐Net model estimates hidden volume loss without chromic ...
Lars Dammann, Ci Song, Bahram Vaghefinazari, Tim Würger, Anna Lisitsyna, Shadi Albarqouni, Mikhail L. Zheludkevich, Sviatlana V. Lamaka, Christian Feiler   +8 more
wiley   +1 more source

Physics‐Guided Modeling of Temperature‐Dependent Electrical Conductivity of Functional Oxides

Advanced Intelligent Discovery, EarlyView.
This complete workflow presents an automated, data‐driven screening of functional oxides based on their predicted electrical conductivity. A curated dataset is fed into TriCondNet, a charge‐transport, physics‐guided neural network that classifies an input composition's conduction mechanism and then simulates its conductivity–temperature profile.
Elias Assaad, Jae Evans, Troy D. Manning, Matthew J. Rosseinsky, Kerys Trathen, Bei Peng, Matthew S. Dyer   +6 more
wiley   +1 more source