Results 151 to 160 of about 526,193 (325)

Semantics of Kinship Terms in Tamil from the Semantic Typology Point of View

open access: yesRussian journal of linguistics: Vestnik RUDN, 2016
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  

Semantic and relation aware neural network model for bi-class multi-relational heterogeneous graphs

open access: yesiScience
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

Conversion Therapy for cT4b and M1 Esophageal Squamous Cell Carcinoma: A Comprehensive Systematic Review

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
We systematically reviewed conversion therapy for esophageal squamous cell carcinoma and propose a response‐based treatment strategy for cT4b and M1 disease. For cT4b, we emphasize definitive chemoradiotherapy with timed re‐evaluation and selective salvage or chemoselection to surgery; for M1, conversion is reserved for limited‐burden responders with ...
Eisuke Booka, Hiroya Takeuchi
wiley   +1 more source

AI‐Enhanced Surface‐Enhanced Raman Scattering for Accurate and Sensitive Biomedical Sensing

open access: yesAdvanced Intelligent Discovery, EarlyView.
AI‐SERS advances spectral interpretation with greater precision and speed, enhancing molecular detection, biomedical analysis, and imaging. This review explores its essential contributions to biofluid analysis, disease identification, therapeutic agent evaluation, and high‐resolution biomedical imaging, aiding diagnostic decision‐making.
Seungki Lee, Rowoon Park, Ho Sang Jung
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Named Entity Recognition Models for Machine Learning Interatomic Potentials: A User‐Centric Approach to Knowledge Extraction from Scientific Literature

open access: yesAdvanced Intelligent Discovery, EarlyView.
Named entity recognition pipeline for knowledge extraction from scientific literature. Machine learning interatomic potential (MLIP) is an emerging technique that has helped achieve molecular dynamics simulations with unprecedented balance between efficiency and accuracy. Recently, the body of MLIP literature has been growing rapidly, which propels the
Bowen Zheng, Grace X. Gu
wiley   +1 more source

Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications

open access: yesAdvanced Intelligent Discovery, EarlyView.
Large language models are transforming microbiome research by enabling advanced sequence profiling, functional prediction, and association mining across complex datasets. They automate microbial classification and disease‐state recognition, improving cross‐study integration and clinical diagnostics.
Jieqi Xing   +4 more
wiley   +1 more source

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