CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction

to related objects

Location: Deutsche Nationalbibliothek Frankfurt am Main

Extent: 1 Online-Ressource.

Language: Englisch

Bibliographic citation: CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction ; volume:25 ; number:1 ; day:2 ; month:4 ; year:2024 ; pages:1-20 ; date:12.2024
BMC bioinformatics ; 25, Heft 1 (2.4.2024), 1-20, 12.2024

Creator: Zeng, Xiaoting
Chen, Weilin
Lei, Baiying

Contributor: SpringerLink (Online service)

DOI: 10.1186/s12859-024-05753-2

URN: urn:nbn:de:101:1-2406162106325.481424324227

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

Last update: 14.08.2025, 10:58 AM CEST

Data provider

This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.

Show original at data provider

Associated

Zeng, Xiaoting
Chen, Weilin
Lei, Baiying
SpringerLink (Online service)

Other Objects (12)

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction

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

Relative molecule self-attention transformer

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

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

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

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

DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction

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

Relative molecule self-attention transformer

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

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

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

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

DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction

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

Relative molecule self-attention transformer

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

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

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

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

DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Cultural heritage institutions wishing to register will find more information here.

Fields marked * need to be filled in.

Username*

Please enter your username

Email*

Please enter your email address

Please do not fill this field

First name

Last name

Password*

Please enter your password

Confirm password*

Please enter the same password

I have read the terms of use and the privacy policy for the collection of personal data and accept them. *

This field is required.

I would like to subscribe to the newsletter of the Deutsche Digitale Bibliothek. See newsletter subscription info.

Account created

Your "My DDB" account has been successfully created. Before you can log in to your account, you must click the confirmation link in the message we just sent to the email address you provided.

CAT-DTI: cross-attention and Transformer network with domain adaptation for drug-target interaction prediction

Object Details

References and Relationships

Contributors, Places and Time

Further information

Data provider

Associated

Other Objects (12)

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction

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

Relative molecule self-attention transformer

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

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

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

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

DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction

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

Relative molecule self-attention transformer

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

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

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

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

DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction

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

Relative molecule self-attention transformer

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

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

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

DTI-HeNE: a novel method for drug-target interaction prediction based on heterogeneous network embedding

Accurate prediction of drug-target interactions in Chinese and western medicine by the CWI-DTI model

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

DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning

MSH-DTI: multi-graph convolution with self-supervised embedding and heterogeneous aggregation for drug-target interaction prediction

DGDTA: dynamic graph attention network for predicting drug–target binding affinity

Related objects

Reset password