ArticleBriefings in bioinformatics2026
K-attention: a biologically informed attention operator for data-efficient sequence-based omics modeling.
Article in Briefings in 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.
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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.
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8 authors.
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Abstract
Deep learning-based modeling of omics data often suffers from insufficiency and heterogeneity of the data itself. As a step towards addressing these issues, we present K-attention, a biologically informed operator that models interactions between sequence fragments effectively and efficiently. Across both biologically informed simulated datasets and two real-world omics tasks, K-attention-based networks consistently outperform canonical convolutional neural network (CNN)- and Transformer-based models, with the largest gains observed in low-data regimes. Collectively, these results indicate that K-attention enables data-efficient and biologically grounded modeling under real-world constraints.
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