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Inferring Negative Molecular Biomarker Data at Scale.

Ashleigh E McBratney1, Benjamin A Holmes1, Giles S Brown1

  • 1Syapse Inc, San Francisco, CA.

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Summary

This study developed a method using natural language processing (NLP) to identify and include negative biomarker results in molecular data. This improves data completeness for a comprehensive understanding of cancer testing landscapes.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Negative biomarker results are crucial for understanding cancer testing but often missing from data.
  • Next-generation sequencing (NGS) panels test many genes, yet explicit negative results are rarely reported.
  • A complete molecular data repository requires inclusion of both positive and negative test findings.

Purpose of the Study:

  • To develop a data pipeline for inferring and incorporating unstated negative biomarker results.
  • To enhance the completeness and clarity of molecular testing data for cancer patients.
  • To create a comprehensive dataset reflecting the full spectrum of molecular testing outcomes.

Main Methods:

  • Utilized natural language processing (NLP), terminology management, and internal rulesets for data transformation.
  • Extracted and transformed laboratory gene panel information into a semistructured format.
  • Developed a normalization ontology to semantically align and infer negative results.

Main Results:

  • Successfully leveraged positive biomarker data to derive negative results.
  • Created a comprehensive dataset for molecular testing paradigms.
  • Achieved a significant improvement in data completeness and clarity compared to existing datasets.

Conclusions:

  • Accurate determination of positivity and testing rates is essential for clinical insights.
  • Inferring negative results allows for a complete analysis of tested patient populations.
  • This approach enables quality checks and monitoring of adherence to testing recommendations.