ArticleBriefings in bioinformatics2025
MAPTrans: mutual attention transformer with dynamic meta-path pruning for drug repositioning.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug repositioning has become a hot topic that could provide an innovative solution in drug discovery by exploring the potential correlation between drugs and diseases. However, existing computational drug repositioning methods fail to effectively integrate heterogeneous data from multiple sources and neglect the multi-level and multi-scale interactions in biological systems. To address the above problems, we propose MAPTrans, which dynamically optimizes the representation of disease and drug with a multi-level meta-path aggregation strategy. In addition, a multi-view importance assessment mechanism is introduced to evaluate and filter the most discriminating views to optimize feature representation. A mutual attention mechanism Transformer architecture with a cross-view interaction that fuses the information of drugs and diseases in a multi-view space is designed. Experimental results of MAPTrans on multiple benchmark datasets show that it significantly outperforms existing baseline models.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.