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Deep learning-assisted literature mining for in vitro radiosensitivity data.

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This study introduces an automated pipeline using deep learning to extract cancer cell radiosensitivity data from scientific literature. This tool significantly aids researchers by efficiently gathering crucial data from clonogenic assays.

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Area of Science:

  • Oncology
  • Bioinformatics
  • Radiotherapy Research

Background:

  • Analyzing radiosensitivity data from clonogenic assays is vital for understanding cancer cell radioresistance.
  • Manual extraction of this data from literature is time-consuming and labor-intensive.

Purpose of the Study:

  • To develop an automated analysis pipeline for extracting radiosensitivity data from scientific literature.
  • To improve the efficiency of data acquisition for radiosensitivity research.

Main Methods:

  • Utilized deep convolutional neural networks (CNNs), including Faster Regions CNN with Inception Resnet v2 (fRCNN-IRv2) and Mask RCNN.
  • Employed Optical Character Recognition (OCR) for data extraction from semi-logarithmic and bar graphs.
  • Developed classifiers (C1-3) to identify relevant publications and a program (iSF2) to extract specific radiosensitivity metrics (SF2).

Main Results:

  • Classifiers C1-3 achieved high sensitivity (91.2%) and specificity (90.7%).
  • The iSF2 program extracted surviving fraction after 2-Gy irradiation (SF2) values with an accuracy of 2.9% compared to expert evaluations.
  • The pipeline demonstrated robust performance across seven datasets.

Conclusions:

  • The developed analysis pipeline effectively automates the acquisition of radiosensitivity data from clonogenic assays in the literature.
  • This tool has the potential to accelerate research into cancer cell radioresistance and inform treatment strategies.