Results 141 to 150 of about 177,158 (279)

Knowledge graph embedding for profiling the interaction between transcription factors and their target genes. [PDF]

PLoS Comput Biol, 2023
Wu YH, Huang YA, Li JQ, You ZH, Hu PW, Hu L, Leung VCM, Du ZH.   +7 more
europepmc   +1 more source

TRMT6‐Mediated m1A Modification of CDK9 mRNA Is a Dual‐Pronged Pathogenic Driver for HBV‐Related Hepatocellular Carcinoma

Advanced Science, EarlyView.
TRMT6‐mediated m1A modification in CDK9 mRNA enhances its mRNA stability and translation efficiency, thereby increasing the protein levels of CDK9. Upregulated CDK9 promotes the progression of HCC by elevating the levels of oncogenic factors including p‐STAT3, MCL1, and BCL‐2. On the other hand, CDK9 phosphorylates TARDBP at Ser254 to activate HBV core
Rui Zhang, Dandan Zong, Rui Liu, Yubo Wang, Qingqing Gu, Yao Yao, Wenfang Zheng, Mengmeng Yuan, Simeng Wang, Rongrong Cui, Daxu Li, Siwen Dang, Peng Hou   +12 more
wiley   +1 more source

Predicting protein and pathway associations for understudied dark kinases using pattern-constrained knowledge graph embedding. [PDF]

PeerJ, 2023
Salcedo MV, Gravel N, Keshavarzi A, Huang LC, Kochut KJ, Kannan N.   +5 more
europepmc   +1 more source

Evaluating the Utilities of Foundation Models in Single‐Cell Data Analysis

Advanced Science, EarlyView.
This study delivers the first systematic, task‐level evaluation of single‐cell foundation models across eight core analytical tasks. By benchmarking 10 leading models with the scEval framework, it reveals where foundation models truly add value, where task‐specific methods still dominate, and provides concrete, reproducible guidelines to steer the next
Tianyu Liu, Kexing Li, Yuge Wang, Hongyu Li, Hongyu Zhao   +4 more
wiley   +1 more source

Combining the External Medical Knowledge Graph Embedding to Improve the Performance of Syndrome Differentiation Model. [PDF]

Evid Based Complement Alternat Med, 2023
Ye Q, Yang R, Cheng CL, Peng L, Lan Y.
europepmc   +1 more source

NIBV Induces Incomplete Autophagy via AMPK‐TFEB, Causing Kidney Injury in Chicks

Advanced Science, EarlyView.
This study reveals that NIBV can inhibit the nuclear translocation of TFEB by suppressing the expression of AMPK, leading to the blockade of autophagolysosomal functions, in turn increasing NIBV replication and triggering severe kidney injury in chicks.
Cheng Huang, Yizhou Zeng, Yunfeng Chen, Zhengqing Li, Salma Mbarouk Omar, Shengwei Zhong, Ping Liu, Zhanhong Zheng, Gaofeng Cai, Xiaona Gao, Xiaoquan Guo   +10 more
wiley   +1 more source

Molecular-evaluated and explainable drug repurposing for COVID-19 using ensemble knowledge graph embedding. [PDF]

Sci Rep, 2023
Islam MK, Amaya-Ramirez D, Maigret B, Devignes MD, Aridhi S, Smaïl-Tabbone M.   +5 more
europepmc   +1 more source

De Novo Multi‐Mechanism Antimicrobial Peptide Design via Multimodal Deep Learning

Advanced Science, EarlyView.
Current AI‐driven peptide discovery often overlooks complex structural data. This study presents M3‐CAD, a generative pipeline that leverages 3D voxel coloring and a massive database of over 12 000 peptides to capture nuanced physicochemical contexts.
Xiaojuan Li, Haifan Gong, Yue Wang, Yinuo Zhao, Lixiang Li, Peijing Bao, Qingzhou Kong, Jialu Fu, Boyao Wan, Yumeng Zhang, Jinghui Zhang, Jiekun Ni, Zhongxue Han, Xueping Nan, Kunping Ju, Longfei Sun, Yuerui Ma, Huijun Chang, Mengqi Zheng, Yanbo Yu, Xiaoyun Yang, Xiuli Zuo, Haina Wang, Yanqing Li   +23 more
wiley   +1 more source

Ensembles of knowledge graph embedding models improve predictions for drug discovery. [PDF]

Brief Bioinform, 2022
Rivas-Barragan D, Domingo-Fernández D, Gadiya Y, Healey D.   +3 more
europepmc   +1 more source

Causal Prediction of TP53 Variant Pathogenicity Using a Perturbation‐Informed Protein Language Model

Advanced Science, EarlyView.
A TP53‐specific predictor, CaVepP53, is developed by fine‐tuning ESMC on experimentally validated variants, quantifying pathogenicity via Euclidean distances. It outperforms general‐purpose models and extends to five cancer genes, enabling interpretable variant classification for precision medicine.
Huiying Chen, Yang Zhao, Boqiang Hu, Wuke Wang, Minfang Song, Annabeth Xinyu Zhao, Xiangyang Li, Gefei Wang, Yanfen Wang, Weiyan Zheng, Xinpeng Zhang, Xia Lin, Yanbin Yin, Xingxu Huang, Jinfang Zheng, Tingbo Liang   +15 more
wiley   +1 more source