Cargando…

Comparative Analysis of Unsupervised Protein Similarity Prediction Based on Graph Embedding

The study of protein–protein interaction and the determination of protein functions are important parts of proteomics. Computational methods are used to study the similarity between proteins based on Gene Ontology (GO) to explore their functions and possible interactions. GO is a series of standardi...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhang, Yuanyuan, Wang, Ziqi, Wang, Shudong, Shang, Junliang
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2021
Materias:	Genetics
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8493040/ https://www.ncbi.nlm.nih.gov/pubmed/34630534 http://dx.doi.org/10.3389/fgene.2021.744334

Ejemplares similares

Predicting lncRNA-protein interactions with bipartite graph embedding and deep graph neural networks
por: Ma, Yuzhou, et al.
Publicado: (2023)

Hierarchical Microbial Functions Prediction by Graph Aggregated Embedding
por: Hou, Yujie, et al.
Publicado: (2021)

An Efficient Computational Model for Large-Scale Prediction of Protein–Protein Interactions Based on Accurate and Scalable Graph Embedding
por: Su, Xiao-Rui, et al.
Publicado: (2021)

The Unsupervised Feature Selection Algorithms Based on Standard Deviation and Cosine Similarity for Genomic Data Analysis
por: Xie, Juanying, et al.
Publicado: (2021)

Editorial: Graph Embedding Methods for Multiple-Omics Data Analysis
por: Lan, Wei, et al.
Publicado: (2021)

DapBCH: a disease association prediction model Based on Cross-species and Heterogeneous graph embedding
por: Shi, Wanqi, et al.
Publicado: (2023)

Graph Embedding Based Novel Gene Discovery Associated With Diabetes Mellitus
por: Du, Jianzong, et al.
Publicado: (2021)

Unsupervised Embedding Learning for Large-Scale Heterogeneous Networks Based on Metapath Graph Sampling
por: Zhong, Hongwei, et al.
Publicado: (2023)

Unsupervised Event Graph Representation and Similarity Learning on Biomedical Literature
por: Frisoni, Giacomo, et al.
Publicado: (2021)

Graph Embedding Deep Learning Guides Microbial Biomarkers' Identification
por: Zhu, Qiang, et al.
Publicado: (2019)

Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype–phenotype interactions
por: Guo, Xinpeng, et al.
Publicado: (2022)

Corrigendum: Graph Embedding Deep Learning Guides Microbial Biomarkers' Identification
por: Zhu, Qiang, et al.
Publicado: (2020)

iLncDA-RSN: identification of lncRNA-disease associations based on reliable similarity networks
por: Li, Yahan, et al.
Publicado: (2023)

Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data
por: Ashoor, Haitham, et al.
Publicado: (2020)

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques
por: Thafar, Maha A., et al.
Publicado: (2020)

Graph-based prediction of Protein-protein interactions with attributed signed graph embedding
por: Yang, Fang, et al.
Publicado: (2020)

Predicting circRNA-Disease Associations Based on circRNA Expression Similarity and Functional Similarity
por: Wang, Yongtian, et al.
Publicado: (2019)

m6AGE: A Predictor for N6-Methyladenosine Sites Identification Utilizing Sequence Characteristics and Graph Embedding-Based Geometrical Information
por: Wang, Yan, et al.
Publicado: (2021)

Predicting lncRNA–Protein Interactions by Heterogenous Network Embedding
por: Zhao, Guoqing, et al.
Publicado: (2022)

ETGPDA: identification of piRNA-disease associations based on embedding transformation graph convolutional network
por: Meng, Xianghan, et al.
Publicado: (2023)

Predicting miRNA-Disease Associations Based on Heterogeneous Graph Attention Networks
por: Ji, Cunmei, et al.
Publicado: (2021)

Prediction of lncRNA–Disease Associations via Closest Node Weight Graphs of the Spatial Neighborhood Based on the Edge Attention Graph Convolutional Network
por: Li, Jianwei, et al.
Publicado: (2022)

MGRL: Predicting Drug-Disease Associations Based on Multi-Graph Representation Learning
por: Zhao, Bo-Wei, et al.
Publicado: (2021)

Predicting lncRNA–Protein Interaction With Weighted Graph-Regularized Matrix Factorization
por: Sun, Xibo, et al.
Publicado: (2021)

VGAEDTI: drug-target interaction prediction based on variational inference and graph autoencoder
por: Zhang, Yuanyuan, et al.
Publicado: (2023)

Tensor Decomposition-Based Unsupervised Feature Extraction Applied to Single-Cell Gene Expression Analysis
por: Taguchi, Y-h., et al.
Publicado: (2019)

InfersentPPI: Prediction of Protein-Protein Interaction Using Protein Sentence Embedding With Gene Ontology Information
por: Li, Meijing, et al.
Publicado: (2022)

Graph embedding-based novel protein interaction prediction via higher-order graph convolutional network
por: Xiao, Ze, et al.
Publicado: (2020)

PGAGP: Predicting pathogenic genes based on adaptive network embedding algorithm
por: Zhang, Yan, et al.
Publicado: (2023)

Antimicrobial Peptides Prediction method based on sequence multidimensional feature embedding
por: Dong, Benzhi, et al.
Publicado: (2022)

NMFNA: A Non-negative Matrix Factorization Network Analysis Method for Identifying Modules and Characteristic Genes of Pancreatic Cancer
por: Ding, Qian, et al.
Publicado: (2021)

SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder
por: Wang, Shudong, et al.
Publicado: (2022)

Graph embeddings on gene ontology annotations for protein–protein interaction prediction
por: Zhong, Xiaoshi, et al.
Publicado: (2020)

Predicting Pseudogene–miRNA Associations Based on Feature Fusion and Graph Auto-Encoder
por: Zhou, Shijia, et al.
Publicado: (2021)

Multi-Similarities Bilinear Matrix Factorization-Based Method for Predicting Human Microbe–Disease Associations
por: Yang, Xiaoyu, et al.
Publicado: (2021)

Embedded graphs
por: Lando, S, et al.
Publicado: (2001)

MSCHLMDA: Multi-Similarity Based Combinative Hypergraph Learning for Predicting MiRNA-Disease Association
por: Wu, Qingwen, et al.
Publicado: (2020)

EOESGC: predicting miRNA-disease associations based on embedding of embedding and simplified graph convolutional network
por: Pang, Shanchen, et al.
Publicado: (2021)

iPoLNG—An unsupervised model for the integrative analysis of single-cell multiomics data
por: Zhang, Wenyu, et al.
Publicado: (2023)

Graph Embedding for Pattern Analysis
por: Fu, Yun, et al.
Publicado: (2013)

Cannot write session to /tmp/vufind_sessions/sess_22tassci7cpakn4hisjgn1kvbc