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CarD-T: Interpreting Carcinomic Lexicon via Transformers.

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  • 1Mechanical Engineering Department, San Diego State University, San Diego, CA, USA.

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Summary

A new framework, Carcinogen Detection via Transformers (CarD-T), uses AI to efficiently identify potential carcinogens in scientific literature. This automated approach aids cancer epidemiology and public health by analyzing vast amounts of data faster than manual methods.

Keywords:
Bayesian analysisBiomedical language modelsCancer epidemiologyCarcinogen identificationNamed Entity Recognition

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Area of Science:

  • Computational toxicology
  • Cancer epidemiology
  • Bioinformatics

Background:

  • Accurate carcinogen identification is crucial for cancer epidemiology.
  • Existing methods face challenges with the increasing volume of biomedical literature.
  • Manual vetting of scientific texts is time-consuming and prone to disparities.

Purpose of the Study:

  • To introduce the Carcinogen Detection via Transformers (CarD-T) framework for automated carcinogen nomination.
  • To improve the efficiency and accuracy of identifying potential carcinogens from scientific texts.
  • To provide a scalable solution for toxicological investigations.

Main Methods:

  • Developed the Carcinogen Detection via Transformers (CarD-T) framework, integrating transformer-based machine learning and probabilistic statistical analysis.
  • Utilized Named Entity Recognition (NER) trained on PubMed abstracts and a context classifier.
  • Analyzed 25 years of journal publication data indexed with carcinogenicity and carcinogenesis MeSH terms.
  • Employed Bayesian temporal Probabilistic Carcinogenic Denomination (PCarD) for analyzing disputing evidence.

Main Results:

  • CarD-T accurately identified all established IARC Group 1 and 2A carcinogens from the test data.
  • Nominated approximately 1500 additional potential carcinogens with supporting publications.
  • Achieved high recall (0.857) and F1 score (0.875) compared to GPT-4.
  • Highlighted 554 entities with disputing evidence for carcinogenicity, further analyzed by PCarD.

Conclusions:

  • The CarD-T framework is a robust, efficient, and scalable tool for identifying potential carcinogens in biomedical literature.
  • This AI-driven approach enhances the agility of public health responses to carcinogen identification.
  • CarD-T sets a new benchmark for automated toxicological investigations, even on consumer GPUs.