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Searching for LINCS to Stress: Using Text Mining to Automate Reference Chemical Curation.

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This study developed an automated pipeline to create a public database of chemicals affecting stress response pathways (SRPs). Machine learning prioritized abstracts, enabling efficient identification of 457 chemicals with SRP activities for public health applications.

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Area of Science:

  • Cellular Biology
  • Toxicology
  • Bioinformatics

Background:

  • Stress response pathways (SRPs) are crucial for cellular homeostasis but can lead to apoptosis, autophagy, or senescence.
  • SRPs are key targets for therapeutic interventions and toxicity biomarkers.
  • A public database of chemicals perturbing SRPs is needed for data-driven public health tools.

Purpose of the Study:

  • To develop and curate the first version of a public database of chemicals that perturb SRPs.
  • To create an automated text-mining pipeline for efficient database construction.
  • To enable new data-driven tools for public health using chemical perturbation data.

Main Methods:

  • Developed an automated text-mining pipeline using reference SRP chemicals and PubMed abstracts.
  • Employed information retrieval and mutual information thresholds to identify relevant chemical-SRP relationships.
  • Utilized machine learning (artificial neural networks) to prioritize abstracts for expert review, achieving an F1-macro of 0.678.

Main Results:

  • Identified 1206 putative SRP perturbagens from the Library of Integrated Network-Based Cellular Signatures (LINCS) dataset.
  • Manually reviewed abstracts prioritized by machine learning, resulting in 457 chemicals annotated with SRP activities.
  • Validated text-mined associations through independent analysis of mechanisms of action and chemical use classes (e.g., heat shock inducers linked to HSP90).

Conclusions:

  • The developed automated pipeline efficiently builds a valuable public database of SRP-perturbing chemicals.
  • This database facilitates novel applications of LINCS data and advances biomedical information extraction.
  • The findings support the use of text mining and machine learning for curating chemical-biology databases.