A Solid-State Nanopore Signal Generator for Training Machine Learning Models
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913781424062464 |
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| author | Johnson, Jaise Galigekere, Chinmayi R Varma, Manoj M |
| author_facet | Johnson, Jaise Galigekere, Chinmayi R Varma, Manoj M |
| contents | Translocation event detection from raw nanopore current signals is a fundamental step in nanopore signal analysis. Traditional data analysis methods rely on user-defined parameters to extract event information, making the interpretation of experimental results sensitive to parameter choice. While Machine Learning (ML) has seen widespread adoption across various scientific fields, its potential remains underexplored in solid-state nanopore research.
In this work, we introduce a nanopore signal generator capable of producing extensive synthetic datasets for machine learning applications and benchmarking nanopore signal analysis platforms. Using this generator, we train deep learning models to detect translocation events directly from raw signals, achieving over 99% true event detection with minimal false positives. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_05466 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Solid-State Nanopore Signal Generator for Training Machine Learning Models Johnson, Jaise Galigekere, Chinmayi R Varma, Manoj M Signal Processing Biological Physics Biomolecules Machine Learning Translocation event detection from raw nanopore current signals is a fundamental step in nanopore signal analysis. Traditional data analysis methods rely on user-defined parameters to extract event information, making the interpretation of experimental results sensitive to parameter choice. While Machine Learning (ML) has seen widespread adoption across various scientific fields, its potential remains underexplored in solid-state nanopore research. In this work, we introduce a nanopore signal generator capable of producing extensive synthetic datasets for machine learning applications and benchmarking nanopore signal analysis platforms. Using this generator, we train deep learning models to detect translocation events directly from raw signals, achieving over 99% true event detection with minimal false positives. |
| title | A Solid-State Nanopore Signal Generator for Training Machine Learning Models |
| topic | Signal Processing Biological Physics Biomolecules Machine Learning |
| url | https://arxiv.org/abs/2504.05466 |