Results 111 to 120 of about 51,918 (265)
SMarT‐Diff introduces a multi‐objective generative paradigm that integrates scaffold hopping with structure‐aware scoring to enable controlled exploration beyond the training distribution. The framework consistently balances drug‐likeness, synthesizes accessibility and bioactivity, yielding chemically diverse candidates with enhanced properties.
Yuwei Yang +8 more
wiley +1 more source
MarginPath is a novel vision‐language system that automates breast cancer margin assessment using a single label‐free multiphoton microscopy image. By integrating tumor‐associated collagen signatures with virtual H&E imaging, it generates accurate margin heatmaps and comprehensive diagnostic reports.
Shu Wang +15 more
wiley +1 more source
A biomimetic artificial intelligence system, PancDS, has been developed to distinguish pancreatic ductal adenocarcinoma from mass‐forming pancreatitis by adaptively integrating clinical data, radiomics, and deep learning features. Validated across multicenter, reader‐study, and prospective settings, PancDS improves diagnostic accuracy, particularly for
Zhibo Wang +13 more
wiley +1 more source
Semantics of Kinship Terms in Tamil from the Semantic Typology Point of View
In this article the author examines the lexical-semantic group “kinship terms” in Tamil, applying the attainments of modern semantic typology and the theory of semantic derivation.
Anna Aleksandrovna Smirnitskaya
doaj
PlantGFM: A Genomic Foundation Model for Discovery and Creation of Plant Genes
A plant genomic foundation model pre‐trained on 12 species enables both accurate gene prediction and de novo gene design. Through AI‐human knowledge screening, seven designed sequences showed transcriptional activity in plants, with two expressing stable proteins—demonstrating the first DNA‐RNA‐protein expression of LLM‐generated genes in plants and ...
Changhao Li +10 more
wiley +1 more source
Relation Extraction Using Semantic Information
Jian Xu, Qin Lu, Minglei Li
doaj +1 more source
Semantic and relation aware neural network model for bi-class multi-relational heterogeneous graphs
Summary: This paper constructs three bi-class multi-relational heterogeneous graphs based on real-world data, and it proposes a semantic and relation aware neural network model (SRA-BMHN) designed for bi-class multi-relational heterogeneous graphs.
Yufei Zhao, Hua Liu, Hua Duan
doaj +1 more source
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
PhosSight is a unified deep‐learning framework for phosphoproteome identification, featured by a phosphorylation‐aware detectability predictor. It improves identification sensitivity in DDA through deep re‐localization and rescoring, accelerates DIA searches by detectability‐guided spectral library pruning, and expands phosphoproteome coverage to ...
Ben Wang +10 more
wiley +1 more source
stMixer for Scalable Mosaic Integration and Label Transfer in Spatial Histology and Multi‐Omics
stMixer is an unsupervised framework for scalable integration and label transfer across spatial histology and multi‐slide multi‐omics data with incomplete modality overlap. It combines self‐looped cross‐attention, multimodal metric learning, and graph‐guided cluster voting to align heterogeneous sections, correct batch effects, and propagate ...
Qixing Yang +3 more
wiley +1 more source

