Link prediction in drug-target interactions network using similarity indices

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1471-2105

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: Link prediction in drug-target interactions network using similarity indices ; volume:18 ; number:1 ; day:17 ; month:1 ; year:2017 ; pages:1-9 ; date:12.2017
BMC bioinformatics ; 18, Heft 1 (17.1.2017), 1-9, 12.2017

Keyword: Biopharmazie
Pharmakodynamik
Pharmakokinetik

Creator: Lu, Yiding

Contributor: Guo, Yufan
Korhonen, Anna
SpringerLink (Online service)

DOI: 10.1186/s12859-017-1460-z

URN: urn:nbn:de:1111-201702137703

Rights: Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 14.08.2025, 10:46 AM CEST

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Lu, Yiding
Guo, Yufan
Korhonen, Anna
SpringerLink (Online service)

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Drug-target interactions prediction using marginalized denoising model on heterogeneous networks

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MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug–Target Interactions Prediction

DASPfind: new efficient method to predict drug–target interactions

Explainable deep drug–target representations for binding affinity prediction

Prediction of drug-target interactions from multi-molecular network based on LINE network representation method

Drug-target interaction prediction via class imbalance-aware ensemble learning

Prediction of drug–target interactions through multi-task learning

Novel drug-target interactions via link prediction and network embedding

Drug-target interactions prediction using marginalized denoising model on heterogeneous networks

Sequence-based prediction of protein binding regions and drug–target interactions

A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Drug–target interaction prediction via multiple classification strategies

MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug–Target Interactions Prediction

DASPfind: new efficient method to predict drug–target interactions

Explainable deep drug–target representations for binding affinity prediction

Prediction of drug-target interactions from multi-molecular network based on LINE network representation method

Drug-target interaction prediction via class imbalance-aware ensemble learning

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Link prediction in drug-target interactions network using similarity indices

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Other Objects (12)

Prediction of drug–target interactions through multi-task learning

Novel drug-target interactions via link prediction and network embedding

Drug-target interactions prediction using marginalized denoising model on heterogeneous networks

Sequence-based prediction of protein binding regions and drug–target interactions

A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Drug–target interaction prediction via multiple classification strategies

MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug–Target Interactions Prediction

DASPfind: new efficient method to predict drug–target interactions

Explainable deep drug–target representations for binding affinity prediction

Prediction of drug-target interactions from multi-molecular network based on LINE network representation method

Drug-target interaction prediction via class imbalance-aware ensemble learning

Prediction of drug–target interactions through multi-task learning

Novel drug-target interactions via link prediction and network embedding

Drug-target interactions prediction using marginalized denoising model on heterogeneous networks

Sequence-based prediction of protein binding regions and drug–target interactions

A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Drug–target interaction prediction via multiple classification strategies

MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug–Target Interactions Prediction

DASPfind: new efficient method to predict drug–target interactions

Explainable deep drug–target representations for binding affinity prediction

Prediction of drug-target interactions from multi-molecular network based on LINE network representation method

Drug-target interaction prediction via class imbalance-aware ensemble learning

Prediction of drug–target interactions through multi-task learning

Novel drug-target interactions via link prediction and network embedding

Drug-target interactions prediction using marginalized denoising model on heterogeneous networks

Sequence-based prediction of protein binding regions and drug–target interactions

A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction

DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques

Drug–target interaction prediction via multiple classification strategies

MocFormer: A Two-Stage Pre-training-Driven Transformer for Drug–Target Interactions Prediction

DASPfind: new efficient method to predict drug–target interactions

Explainable deep drug–target representations for binding affinity prediction

Prediction of drug-target interactions from multi-molecular network based on LINE network representation method

Drug-target interaction prediction via class imbalance-aware ensemble learning

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