@inproceedings{kondadadi-ortega-2026-beyond,
    title = "Beyond Knowledge Graphs: {P}ub{M}ed{BERT} Embeddings as a Competitive Standalone Modality for Drug Re-purposing",
    author = "Kondadadi, Rishik  and
      Ortega, John E.",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.13/",
    pages = "135--140",
    ISBN = "979-8-89176-434-7",
    abstract = "Drug repurposing methods rely heavily on knowledge graph (KG) embeddings, but building and curating these graphs takes considerable effort. We present two findings on the Hetionet drug-disease benchmark and an epilepsy ranking task. First, PubMedBERT text embeddings, fed through the same downstream classifiers and identical 10-fold splits as four re-trained KG baselines (TransE, ComplEx, DistMult, RotatE), reach AUROC {\$}0.910{\$}, above all four (best: RotatE, {\$}0.854{\$}); a Random Forest on the same vectors scores {\$}0.880{\$}. The comparison is asymmetric in one important way: PubMedBERT was pretrained on the literature Hetionet was curated from, so the result is best read as ``text-with-literature-supervision vs.graph-only,'' and a head-to-head with text-augmented KG methods (KG-BERT, TxGNN) is left as follow-up. Second, across all seven combinations of text, molecular (ECFP4), and gene expression (LINCS L1000) features, cross-attention fusion of weaker modalities into text consistently degrades performance, despite a gated mechanism intended to suppress unhelpful modalities; the residual path forces the strong modality to absorb noise. The model also ranks proconvulsants (amoxapine, flumazenil) near the top, because text embeddings encode strength of association with a disease but not its direction."
}