Literature-informed gene extraction and ranking for multimodal data fusion

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ID: 319695
2026
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Abstract
Published biomedical experiments provide an increasingly rich collection of results and identify genes potentially involved in diverse biological mechanisms. However, individual studies are often confined to narrow experimental contexts and are restricted to single omics layers. Cross-study knowledge aggregation can broaden this perspective and enable the construction of global, context-aware gene rankings. Recent developments in natural language processing have made large-scale literature mining increasingly feasible. This enables the systematic extraction and fusion of symbolic knowledge from published experiments. We present pathXcite, a software that extracts genes associated with specific contexts, such as diseases or biological mechanisms from the literature, and ranks them by contextual relevance. These relevance-based gene rankings can compress a scientific context into a symbolic representation. This representation enables diverse downstream analyses, including cross-context comparisons, network-based analysis, enrichment analysis, and integration with experimental omics data. In multiple use cases, we show how our extraction and fusion strategy can be applied to uncover hidden aspects in biological data.
Reference Key
openalex_W7167430732 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Marietta Hamberger, Silke D Werle, Johann M. Kraus, Hans A. Kestler
Journal Briefings in bioinformatics
Year 2026
DOI
10.1093/bib/bbag348
URL
https://doi.org/10.1093/bib/bbag348
Keywords Keywords not found

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