Multi-modal transformer for signal classification in nanopore blockade experiments
Ranking
Overall
72
Content
85
Popularity
43
Observed public metrics from 1 member.
Merged summary
TL;DR - A multimodal transformer combines nanopore time series, wavelet images, and static features to classify molecular signals. It substantially improves peptide identification and could support robust, portable biomarker diagnostics.
- Outperforms prior methods by over 10 percentage points on a 42-peptide benchmark.
- Transfers to a 20-amino-acid dataset with near-perfect accuracy.
- Attention analysis indicates that time-series and wavelet inputs capture complementary signal characteristics.
Sources (1)
Multi-modal transformer for signal classification in nanopore blockade experiments
Public signals
Semantic Scholar citations 0 · Semantic Scholar influential citations 0
TL;DR - A multimodal transformer combines nanopore time series, wavelet images, and static features to classify molecular signals. It substantially improves peptide identification and could support robust, portable biomarker diagnostics.
- Outperforms prior methods by over 10 percentage points on a 42-peptide benchmark.
- Transfers to a 20-amino-acid dataset with near-perfect accuracy.
- Attention analysis indicates that time-series and wavelet inputs capture complementary signal characteristics.