Evidence map›Paper›PMID 42337424›Full record

ArticleBMC bioinformatics2026

Dual channel drug-drug interactions extraction based on cross attention.

Yaxun Jia, Zhu Yuan, Yunchao Gong, Haixiang Yang, Zuo-Lin Xiang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC bioinformatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yaxun JiaDepartment of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China.
Zhu YuanDepartment of Information Management, The National Police University for Criminal Justice, Baoding, China.
Yunchao GongDepartment of Computer College, Qinghai Normal University, Xining, China.
Haixiang YangBig Data Center of the Ministry of Public Security, Beijing, China.
Zuo-Lin XiangDepartment of Radiation Oncology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, China. xiangzuolinmd@hotmail.com.

Funding

Doctoral Research Start-up Fund Program of The National Police University for Criminal Justice BSQDW202150Key Project of Clinical Research of Shanghai East Hospital, Tongji University DFLC2022012Key Specialty Construction Project of Shanghai Pudong New Area Health Commission PWZzk2022- 02National Natural Science Foundation of China 82160591Outstanding Leaders Training Program of Pudong Health Bureau of Shanghai PWR12023-02Science Research Project of Hebei Education Department QN2025011Shanghai Science and Technology Innovation Action Plan 23Y11909000
6 · The paper itself

Abstract

Drug-drug interactions (DDIs) play a critical role in several biomedical applications, particularly in pharmacovigilance. While neural networks have shown promise in DDI extraction, existing methods often overlook salient information. To address this challenge, we propose a two-channel DDI relationship extraction model based on cross attention - DCCA-IE. This model consists of a sequence channel using BioBERT-CNN and a graph channel integrating BioBERT-LSTM-GAT. The dual channel architecture is crucial to capture both sequential and relational information present in biomedical texts. Furthermore, by employing a cross attention mechanism, we effectively integrate information from the dual channels, resulting in a comprehensive representation for DDI relationship extraction. Experimental evaluation on a benchmark dataset shows that our method outperforms state-of-the-art methods in DDI extraction.

Indexed as

Computational BiologyData MiningNeural Networks, ComputerDrug InteractionsHumansPharmacovigilanceBioBERTCross attentionDrug-drug interaction extractionDual channel mechanismsGraph attention network

Identifiers

PMID42337424
PMCPMC13551928

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.