Results 131 to 140 of about 7,362,198 (297)

Multimodal Cross‐Attentive Graph‐Based Framework for Predicting In Vivo Endocrine Disruptors

Advanced Science, EarlyView.
A multimodal cross‐attentive graph neural network integrates molecular graphs with androgen and estrogen adverse outcome pathway (AOP)–anchored in vitro assay signals to predict in vivo endocrine disruption. By fusing information on Tier‐1 AOP logits with chemical structures, the framework achieves high accuracy and provides assay‐traceable ...
Eder Soares de Almeida Santos, Gustavo Felizardo Santos Sandes, Artur Christian Garcia da Silva, Holli‐Joi Martin, Eugene N. Muratov, Rodolpho de Campos Braga, Bruno Junior Neves   +6 more
wiley   +1 more source

Exploring Korean adolescent stress on social media: a semantic network analysis. [PDF]

PeerJ, 2023
Song J, Yang J, Yoo S, Cheon K, Yun S, Shin Y.   +5 more
europepmc   +1 more source

Multimodal AI‐Driven Identification of Dehydrocostus Lactone as a Potent Renal Fibrosis Attenuator Targeting IQGAP1

Advanced Science, EarlyView.
Renal fibrosis, a hallmark of CKD, lacks effective treatments. Herein, we developed a multimodal AI model (TCM‐SPred) to identify anti‐fibrotic agents and found that dehydrocostus lactone (DCL) targets IQGAP1 to inhibit Wnt signaling, blocking the interaction between IQGAP1 and CCT3, demonstrating potent anti‐fibrotic activity in vitro and in vivo ...
Weijiang Lin, Wenzhuo Xu, Kang Liu, Ping Wang, Zhenzhen Zhu, Wenyu Lu, Zhe Zheng, Xiaoqian Peng, Xunkai Yin, Shulan Mei, An Pan, Jian Liu, Lihong Hu   +12 more
wiley   +1 more source

Ecosystem Services: A Social and Semantic Network Analysis of Public Opinion on Twitter. [PDF]

Int J Environ Res Public Health, 2022
Bruzzese S, Ahmed W, Blanc S, Brun F.
europepmc   +1 more source

MicrobeDiscover: A Knowledge Graph–Enabled AI Framework for Identifying Microbes for Inorganic Nanomaterial Biosynthesis

Advanced Science, EarlyView.
Microbial synthesis of nanomaterials (NMs) is eco‐friendly, but the screening of microorganisms is limited by inefficient traditional methods (currently only involving∽400 microorganisms/90 NMs). We propose AI framework MicrobeDiscover, integrating a knowledge graph of microbe‐NM interactions.
Ludi Wang, Hexing Han, Yufeng Liu, Zhiyuan Ning, Yujie Ma, Haidan Wang, Jinling Xu, Qiansheng Huang, Wenjuan Cui, Yuanchun Zhou, Yang Gao, Bin Wang, Yi Du   +12 more
wiley   +1 more source

Semantic network activation facilitates oral word reading in chronic aphasia. [PDF]

Brain Lang, 2022
Pillay SB, Gross WL, Heffernan J, Book DS, Binder JR.   +4 more
europepmc   +1 more source

Integrating Spatial Proteogenomics in Cancer Research

Advanced Science, EarlyView.
Xx xx. ABSTRACT Background: Spatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
Yida Wang, Yang Wu, Feng Zhang, Parthiban Periasamy, Haiyue You, Denise Goh, Rachel Elizabeth Ann Fincham, Xin Ning, Danping Wu, Lu Liu, Ying Jiang, Zhiwen Qian, Joe Yeong, Yan Zhang   +13 more
wiley   +1 more source

Stable organization of the early lexical-semantic network in 18- and 24-month-old preterm and full-term infants: an eye-tracker study. [PDF]

Front Psychol, 2023
Ragó A, Varga Z, Szabo M.
europepmc   +1 more source

Machine Learning‐Guided Engineering of Protein Phase Separation Properties in Immune Regulation

Advanced Science, EarlyView.
PScalpel, a machine learning model integrating protein structure extraction, graph contrastive learning, and a genetic algorithm, guides the engineering of protein phase separation ability. It adopts transfer learning methods to provide predictive recommendations for protein phase separation ability changes through single amino acid mutations in a ...
Chenqiu Zhang, Jia Wang, Zhe Wang, Liyan Zhu, Sihui Cai, Luzhi Zhan, Haorui Liang, Yaoxing Wu, Jianqiang Li, Jun Cui   +9 more
wiley   +1 more source

Polysemy of the Preposition “be” in Persian Based on Cognitive Semantics [PDF]

مطالعات زبان‌‌ها و گویش‌های غرب ایران, 2015
Studying prepositions in Persian can be a step to recognize the complexity of Persian language which will also help to explain its structure. The purpose of this research is to study different meanings of Persian preposition “be” according to cognitive ...
Hossein Razavian, Masoume Khanzade
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