Results 141 to 150 of about 3,760 (232)

AISyst: AI‐Powered Interactive Visual System to Assist With Fidelity Assessment of Synthetic Tabular Data

Expert Systems, Volume 43, Issue 9, September 2026.
ABSTRACT Evaluating synthetic data produced by generative models remains a critical challenge in sensitive domains such as healthcare and finance. Ensuring that such data is ‘faithful’ to real data is essential for downstream applications and decision‐making, including regulatory compliance. This paper introduces an AI‐powered interactive visual system—
Liqun Liu, Leonid Bogachev, Netochukwu Onyiaji, Lukas Čironis, János Gyarmati‐Szabó, Roy A. Ruddle   +5 more
wiley   +1 more source

Identifying batch-integrated domains from spatial transcriptomics via graph autoencoder with contrastive learning based on cross-modality and data augmentation. [PDF]

Brief Bioinform
Mao Y, Quan L, Chen X, Wang S, Zhang G, Li S, Jiang Y, Nie L, Wu T, Meng L, Lyu Q.   +10 more
europepmc   +1 more source

From knowledge graph to topological data analysis: a novel framework to analyze gene regulatory networks for tomato–multi‐pathogen interactions

New Phytologist, Volume 251, Issue 5, Page 3039-3055, September 2026.
GENIAL framework. Summary of the main steps of the framework to infer and analyze GRN. Summary Tomato (Solanum lycopersicum), despite being the most important vegetable crop world‐wide, remains vulnerable to over 200 diseases caused by different pests.
Maxime Multari, Mathieu Carrière, Xavier Amorós‐Gabarrón, Krishna Kumar, Pratila Debnath, Alexina Damy, Ludo Andrianirina Mamisoa, Sebastian Lobentanzer, Julio Saez‐Rodriguez, Stéphanie Jaubert, Aurélien Dugourd, Justyna J. Olas, Silvia Bottini   +12 more
wiley   +1 more source

A Supervised Contrastive Variational Autoencoder with Probabilistic Latent Alignment for Cross-Domain EEG Emotion Recognition. [PDF]

Sensors (Basel)
Wu L, Yang Y, Wang W, Xie Y, Zhou N, Shi K.   +5 more
europepmc   +1 more source

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

Advanced Science, Volume 13, Issue 44, 7 August 2026.
This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley   +1 more source

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

Graph latent diffusion-based molecular representation learning for enhanced generalization in molecular property prediction. [PDF]

J Cheminform
Koge D, Ono N, Abe T, Kanaya S.
europepmc   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

DCVBin: a novel binning method for single-sample metagenomes based on DNA language model and variational autoencoder. [PDF]

Brief Bioinform
Wang J, Liu Y, Liu F, Hou T, Chen S, Liu S, Liu Y.   +6 more
europepmc   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, Volume 2, Issue 4, August 2026.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source