Causal enhanced drug–target interaction prediction based on graph generation and multi-source information fusion

Authors

Guanyu Qiao (ORCID: https://orcid.org/0000-0001-8128-9173)
Yang Li (ORCID: https://orcid.org/0000-0002-0403-7287)
Guohua Wang (ORCID: https://orcid.org/0000-0001-7381-2374)

Institutions

Harbin Institute of Technology (CN)
Northeast Forestry University (CN)

Publication Details

Journal: Bioinformatics
Published: 2024-09-23
DOI: https://doi.org/10.1093/bioinformatics/btae570
Citations: 17
Primary Topic: Computational Drug Discovery Methods
Type: article
Field-Weighted Citation Impact: 6.14

Funders

National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities

Controls

|||

ALL TIME

JAN

FEB

MAR

APR

MAY

JUN

JUL

AUG

SEP

OCT

article

Causal enhanced drug–target interaction prediction based on graph generation and multi-source information fusion

Guanyu Qiao, Yang Li, Guohua Wang

Sep 23, 2024

17 citations

Bioinformatics

Computational Drug Discovery Methods

6.14

article

Causal enhanced drug–target interaction prediction based on graph generation and multi-source information fusion

Guanyu Qiao, Yang Li, Guohua Wang

September 23, 2024article en

17 citations

Abstract

No abstract available for this paper.

BioinformaticsVol. 40(10)

Harbin Institute of Technology (CN), Northeast Forestry University (CN)

National Natural Science Foundation of China, Fundamental Research Funds for the Central Universities

Openalex Percentile: Top 3%

Computational Drug Discovery Methods

6.14

DOI Open Access PDF OpenAlex

AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.