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0338cfed77482bfc345f · StratoMod: predicting sequencing and variant calling errors with interpretable machine learning

StratoMod: predicting sequencing and variant calling errors with interpretable machine learning

Despite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their

Официальный источник · 2024-10-13

Полные импортированные сведения карточки
{
  "title_en": "StratoMod: predicting sequencing and variant calling errors with interpretable machine learning",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2024-10-13",
  "date_original": "October 13, 2024",
  "url": "https://www.nist.gov/publications/stratomod-predicting-sequencing-and-variant-calling-errors-interpretable-machine",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Nathan Dwarshuis , Nathanael Olson , Fritz Sedlazeck, Justin Wagner , Justin Zook",
  "abstract_excerpt_en": "Despite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their",
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      "title_en": "StratoMod: predicting sequencing and variant calling errors with interpretable machine learning",
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      "date_original": "October 13, 2024",
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      "source": "NIST Publications",
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      "authors_en": "Nathan Dwarshuis , Nathanael Olson , Fritz Sedlazeck, Justin Wagner , Justin Zook",
      "abstract_excerpt_en": "Despite the variety in sequencing platforms, mappers, and variant callers, no single pipeline is optimal across the entire human genome. Therefore, developers, clinicians, and researchers need to make tradeoffs when designing pipelines for their"
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