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In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
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DrugFormer: Graph-Enhanced Language Model to Predict Drug Sensitivity.

Xiaona Liu1, Qing Wang2, Minghao Zhou2

  • 1Center for Computational Systems Medicine, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
|August 29, 2024
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DrugFormer, a new AI model, predicts drug resistance in single cells by analyzing gene data. This tool helps identify resistant cells and potential drug targets for personalized cancer treatments.

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drug resistanceknowledge graphlanguage modelsingle‐cell RNA sequencing

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Area of Science:

  • Computational biology
  • Genomics
  • Artificial intelligence in medicine

Background:

  • Drug resistance significantly limits chemotherapy and targeted therapy efficacy.
  • Tumor cell heterogeneity complicates personalized treatment strategies.
  • Predicting individual drug resistance remains a critical unmet need in oncology.

Purpose of the Study:

  • To introduce DrugFormer, a novel graph-augmented large language model.
  • To predict drug resistance at the single-cell level accurately.
  • To uncover molecular mechanisms and identify therapeutic targets for overcoming drug resistance.

Main Methods:

  • DrugFormer integrates serialized gene tokens and gene-based knowledge graphs.
  • The model was trained on comprehensive single-cell RNA sequencing (scRNA-seq) data with drug response information.
  • Analysis included pseudotime trajectory analysis on patient-derived data.

Main Results:

  • DrugFormer demonstrated superior performance in predicting drug response, with improved F1, precision, and recall.
  • The model effectively identified drug-resistant cells in multiple myeloma (MM) and acute myeloid leukemia (AML) patient data.
  • Drug-resistant cellular states linked to poor outcomes were revealed, and potential therapeutic targets like COX8A were identified.

Conclusions:

  • DrugFormer offers a significant advancement in predicting drug resistance at the single-cell level.
  • The model provides a powerful tool for understanding cellular response heterogeneity to drugs.
  • DrugFormer facilitates the development of personalized treatment strategies to overcome drug resistance.