Results 211 to 220 of about 426,507 (329)
Gene Ontology Driven Feature Selection from Microarray Gene Expression Data [PDF]
Jianlong Qi, Jian Tang
openalex +1 more source
LincNEAT1 Encoded‐NEAT1‐31 micropeptide directly binds with Aurora‐A and enhanced AKT pathways to pormotes phagocytosis against multi cancer cells. Abstract Macrophages play vital roles in innate and adaptive immunity, and their essential functions are mediated by phagocytosis and antigen presentation.
Jie Li+8 more
wiley +1 more source
GOBoost: leveraging long-tail gene ontology terms for accurate protein function prediction. [PDF]
Zhang L+8 more
europepmc +1 more source
Comparison of the Data-based and Gene Ontology-Based Approaches to Cluster Validation Methods for Gene Microarrays [PDF]
Nadia Bolshakova+2 more
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Identifying biomarkers associated with PTC, particularly those related to PTMC progression, is crucial for precise risk stratification and treatment planning. This study utilized single‐cell RNA sequencing on 19 surgical tissue specimens, confirmed PROS1/MERTK axis as a critical component of the cellular microenvironment and a key regulatory mechanism ...
Wenqian Zhang+11 more
wiley +1 more source
GeOKG: geometry-aware knowledge graph embedding for Gene Ontology and genes. [PDF]
Jeong CU, Kim J, Kim D, Sohn KA.
europepmc +1 more source
Comparative analysis of gene ontology-based semantic similarity measurements for the application of identifying essential proteins. [PDF]
Xue X, Zhang W, Fan A.
europepmc +1 more source
Enhanced Media Optimize Bovine Myogenesis in 2D and 3D Models for Cultivated Meat Applications
This study uses multiomics approaches to characterize bovine myogenesis in vitro, identifying media components that enhance myogenic progenitor cell differentiation in 2D and 3D models. Notably, the enhanced conditions promoted formation of unique cell populations and contractile muscle cells expressing mature myogenic markers.
Christine L. Trautmann+4 more
wiley +1 more source
A multi-objective evolutionary algorithm for detecting protein complexes in PPI networks using gene ontology. [PDF]
Abbas MN, Broneske D, Saake G.
europepmc +1 more source