Parameter-efficient fine-tuning on large protein language models improves signal peptide prediction

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Figure 3.
Figure 3.

The sequence logo of Sec/SPII generated with (A) known Sec/SPII sequences (represented by information content) and (B) predicted Sec/SPII sequence patterns via LoRA-tuned ESM2-3B (represented by attention weights). (C) Comparison between the gold-standard sequence logo and the predicted sequence pattern using Spearman's rank correlation between information content and attention weights. Each dot represents one position on the sequence.

This Article

  1. Published in Advance July 26, 2024, doi: 10.1101/gr.279132.124 Genome Res. 34: 1445-1454

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