RNS scores of protein embeddings along with the computed JS divergence and Alignment matches of their sequences.

Embeddings produced by language models (LMs) are widely used as numerical representations of natural language sentences and structured data. However, using embeddings without accounting for model confidence is a critical limitation. The <b>Random Neighbor Score (RNS)</b> provides a model- and task-agnostic measure of embedding uncertainty.<b>Associated Preprint</b>: https://www.biorxiv.org/content/10.1101/2025.04.30.651545v1<b>Files included</b>:<b>File 1</b>: Consolidated sheet containing RNS scores, sequences, alignment results, and Jensen–Shannon divergence values.Columns labeled <code>RNS_IS_*</code> and <code>RNS_ISb_*</code> correspond to RNS values computed at different <i>k</i> settings using <b>Astral40R</b> and <b>Proteome4R</b> as random sets, respectively.<b>File 2</b>: FASTA file of the random sequence set (<b>Astral40R</b>).<b>Sequence sources</b> (see manuscript for details):ASTRAL 40: https://scop.berkeley.edu/astral/Novel Meta set / Orphan set: https://10.0.23.196/m9.figshare.c.6737127Novel Hallucination set: https://www.nature.com/articles/s41586-021-04184-w#data-availability → https://files.ipd.uw.edu/pub/trRosetta/hallucinations2K.tar.gzIntrinsically Disordered Proteins (IDP) &amp; Intrinsically Disordered Regions (IDR): https://disprot.org/download (version 2024_12)