An ensemble-based drug–target interaction prediction approach using multiple feature information with data balancing

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Location: Deutsche Nationalbibliothek Frankfurt am Main

ISSN: 1754-1611

Extent: Online-Ressource

Language: Englisch

Notes: online resource.

Bibliographic citation: An ensemble-based drug–target interaction prediction approach using multiple feature information with data balancing ; volume:16 ; number:1 ; day:8 ; month:8 ; year:2022 ; pages:1-14 ; date:12.2022
Journal of biological engineering ; 16, Heft 1 (8.8.2022), 1-14, 12.2022

Creator: El-Behery, Heba
Attia, Abdel-Fattah
El-Fishawy, Nawal
Torkey, Hanaa

Contributor: SpringerLink (Online service)

DOI: 10.1186/s13036-022-00296-7

URN: urn:nbn:de:101:1-2022102423164369820191

Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.

Last update: 15.08.2025, 7:35 AM CEST

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El-Behery, Heba
Attia, Abdel-Fattah
El-Fishawy, Nawal
Torkey, Hanaa
SpringerLink (Online service)

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An ensemble-based drug–target interaction prediction approach using multiple feature information with data balancing

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Other Objects (12)

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CCL-DTI: contributing the contrastive loss in drug–target interaction prediction

A comparison of embedding aggregation strategies in drug–target interaction prediction

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

A multiple kernel learning algorithm for drug-target interaction prediction

CCL-DTI: contributing the contrastive loss in drug–target interaction prediction

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A learning-based method for drug-target interaction prediction based on feature representation learning and deep neural network

GSRF-DTI: a framework for drug-target interaction prediction based on a drug-target pair network and representation learning on a large graph

Drug–target interaction prediction based on protein features, using wrapper feature selection

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Drug–target interaction prediction via multiple classification strategies

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A multiple kernel learning algorithm for drug-target interaction prediction

CCL-DTI: contributing the contrastive loss in drug–target interaction prediction

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Deep learning-based transcriptome data classification for drug-target interaction prediction

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