PLysPTM-HGNN: predicting lysine PTM sites of proteins using hybrid graph neural networks. [PDF]
Chen L, Yang J, Zhou B, Cai YD.
europepmc +1 more source
Embedded CRISPRi Enhances Gene‐Silencing Efficiency in Drosophila
Current CRISPR interference (CRISPRi) technology in Drosophila has limited efficiency. This study introduces the emCRISPRi platform, which significantly enhances transcriptional silencing efficacy by embedding inhibitory domains within the dCas9 architecture.
Pengchong Fu +7 more
wiley +1 more source
Retraction Note: Knowledge graph driven medicine recommendation system using graph neural networks on longitudinal medical records. [PDF]
Mishra R, Shridevi S.
europepmc +1 more source
This work introduces an open‐source all‐optical platform for functional phenotyping of human stem cell‐derived neurons. The system integrates optogenetics, calcium imaging, automated acquisition, and analysis to resolve single‐cell and network activity, enabling longitudinal measurements, disease modeling, and pharmacological screening in preclinical ...
Wardiya Afshar‐Saber +12 more
wiley +1 more source
Efficient Learning of Molecular Properties Using Graph Neural Networks Enhanced with Chemistry Knowledge. [PDF]
Lutchyn T, Mardal M, Ricaud B.
europepmc +1 more source
A Tac1‐Expressing Brainstem Pathway Underlies the Pathogenesis of Trigeminal Neuralgia
A critical TG‐Sp5CTac1‐PBNTac1 pathway drives trigeminal neuropathic pain (TNP). Tac1‐expressing parabrachial nucleus (PBNTac1) neurons exhibit heightened responses to innocuous stimuli in TNP, and chemogenetic inhibition of these neurons effectively prevents TNP development.
Liting Sun +11 more
wiley +1 more source
Fine-grained causal effect estimation of learning resources via dynamic causal heterogeneous graph neural networks. [PDF]
Ren Y, Chen Z, Jiang X, Du Z.
europepmc +1 more source
Unveil Fundamental Graph Properties for Neural Architecture Search
This paper proposes NASGraph, a graph‐based framework that represents neural architectures as graphs whose structural properties determine performance. By revealing structure–performance relationships, NASGraph enables efficient neural architecture search with significantly reduced computation.
Zhenhan Huang +4 more
wiley +1 more source
Prediction of peptide cleavage sites using protein language models and graph neural networks. [PDF]
Cifuentes P, Adàlia R, Zamora I.
europepmc +1 more source

