ArticleACS omega2026
BAN-SDBPred: Improving Single-Stranded and Double-Stranded DNA-Binding Protein Prediction Using an Attention Network with Bilinear Convolution and Adaptive Sampling Strategy.
Article in ACS omega, 2026. 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
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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.
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0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNA binding proteins play essential roles in numerous biological mechanisms. The DBPs can be either single-stranded binding (SSBs) or double-stranded binding (DSBs) to a DNA molecule. The in-depth identification of SSBs and DSBs has been a hot topic in bioinformatics and is involved in the drug discovery process. Traditional experimental methods failed to characterize the types of DBPs because of high cost and time constraints. While computational prediction of novel SSBs and DSBs has made significant progress, there are still challenges remaining in enhancing overall prediction performance.
Identifiers
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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.