Cargando…

Novel drug-target interactions via link prediction and network embedding

BACKGROUND: As many interactions between the chemical and genomic space remain undiscovered, computational methods able to identify potential drug-target interactions (DTIs) are employed to accelerate drug discovery and reduce the required cost. Predicting new DTIs can leverage drug repurposing by i...

Descripción completa

Detalles Bibliográficos
Autores principales:	Amiri Souri, E., Laddach, R., Karagiannis, S. N., Papageorgiou, L. G., Tsoka, S.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2022
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978405/ https://www.ncbi.nlm.nih.gov/pubmed/35379165 http://dx.doi.org/10.1186/s12859-022-04650-w

Ejemplares similares

Drug repurposing and prediction of multiple interaction types via graph embedding
por: Amiri Souri, E., et al.
Publicado: (2023)

Cancer Grade Model: a multi-gene machine learning-based risk classification for improving prognosis in breast cancer
por: Amiri Souri, E., et al.
Publicado: (2021)

Anchor Link Prediction across Attributed Networks via Network Embedding
por: Wang, Shaokai, et al.
Publicado: (2019)

iDrug: Integration of drug repositioning and drug-target prediction via cross-network embedding
por: Chen, Huiyuan, et al.
Publicado: (2020)

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding
por: Yue, Yang, et al.
Publicado: (2021)

A Novel Temporal Network-Embedding Algorithm for Link Prediction in Dynamic Networks
por: Abbas, Khushnood, et al.
Publicado: (2023)

EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction
por: Jin, Yuan, et al.
Publicado: (2021)

MOKPE: drug–target interaction prediction via manifold optimization based kernel preserving embedding
por: Binatlı, Oğuz C., et al.
Publicado: (2023)

DTi2Vec: Drug–target interaction prediction using network embedding and ensemble learning
por: Thafar, Maha A., et al.
Publicado: (2021)

Link prediction in drug-target interactions network using similarity indices
por: Lu, Yiding, et al.
Publicado: (2017)

Prediction of Drug–Target Interactions From Multi-Molecular Network Based on Deep Walk Embedding Model
por: Chen, Zhan-Heng, et al.
Publicado: (2020)

FLONE: fully Lorentz network embedding for inferring novel drug targets
por: Yue, Yang, et al.
Publicado: (2023)

Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks
por: Yan, Xiao-Ying, et al.
Publicado: (2021)

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

IgE Activates Monocytes from Cancer Patients to Acquire a Pro-Inflammatory Phenotype
por: Nakamura, Mano, et al.
Publicado: (2020)

Prediction of Drug–Target Interaction Using Dual-Network Integrated Logistic Matrix Factorization and Knowledge Graph Embedding
por: Li, Jiaxin, et al.
Publicado: (2022)

Drug-Drug Interaction Predictions via Knowledge Graph and Text Embedding: Instrument Validation Study
por: Wang, Meng, et al.
Publicado: (2021)

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
por: Cheng, Feixiong, et al.
Publicado: (2012)

Community Structure Detection for Overlapping Modules through Mathematical Programming in Protein Interaction Networks
por: Bennett, Laura, et al.
Publicado: (2014)

MSEDDI: Multi-Scale Embedding for Predicting Drug—Drug Interaction Events
por: Yu, Liyi, et al.
Publicado: (2023)

Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interaction
por: Kim, QHwan, et al.
Publicado: (2021)

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

Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks
por: Song, Fei, et al.
Publicado: (2022)

Dual network embedding for representing research interests in the link prediction problem on co-authorship networks
por: Makarov, Ilya, et al.
Publicado: (2019)

Drug-therapy networks and the prediction of novel drug targets
por: Spiro, Zoltan, et al.
Publicado: (2008)

LaGAT: link-aware graph attention network for drug–drug interaction prediction
por: Hong, Yue, et al.
Publicado: (2022)

Complex Embedding with Type Constraints for Link Prediction
por: Li, Xiaohui, et al.
Publicado: (2022)

Network-based piecewise linear regression for QSAR modelling
por: Cardoso-Silva, Jonathan, et al.
Publicado: (2019)

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings
por: Celebi, Remzi, et al.
Publicado: (2019)

Enhancing drug–drug interaction prediction by three-way decision and knowledge graph embedding
por: Hao, Xinkun, et al.
Publicado: (2022)

Hierarchically embedded interaction networks represent a missing link in the study of behavioral and community ecology
por: Montiglio, P O, et al.
Publicado: (2020)

Network-Based Methods for Prediction of Drug-Target Interactions
por: Wu, Zengrui, et al.
Publicado: (2018)

Minimum curvilinearity to enhance topological prediction of protein interactions by network embedding
por: Cannistraci, Carlo Vittorio, et al.
Publicado: (2013)

PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network
por: Wang, Xiaowen, et al.
Publicado: (2022)

NEXGB: A Network Embedding Framework for Anticancer Drug Combination Prediction
por: Meng, Fanjie, et al.
Publicado: (2022)

Module detection in complex networks using integer optimisation
por: Xu, Gang, et al.
Publicado: (2010)

Effective drug–target interaction prediction with mutual interaction neural network
por: Li, Fei, et al.
Publicado: (2022)

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

Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks
por: Hu, ShanShan, et al.
Publicado: (2019)

Drug–target interaction prediction via multiple classification strategies
por: Ye, Qing, et al.
Publicado: (2022)

Cannot write session to /tmp/vufind_sessions/sess_nl5q9qkd5a7ub0sa20td6ssl6s