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Deep learning identifies heterogeneous subpopulations in breast cancer cell lines.

Tyler A JostAndrea L GardnerDaylin MorganAmy Brock
Published in: bioRxiv : the preprint server for biology (2024)
using convolutional neural networks. First, we find that changes induced by chemotherapy treatment are highly identifiable in a breast cancer cell line. We then show that the intra cell line subpopulations that comprise breast cancer cell lines under standard growth conditions are also identifiable using cell morphology. We find that cell morphology is influenced by neighborhood effects beyond the cell boundary, and that including image information surrounding the cell can improve model discrimination ability.
Keyphrases
  • deep learning
  • single cell
  • convolutional neural network
  • cell therapy
  • healthcare
  • squamous cell carcinoma
  • artificial intelligence
  • dna methylation
  • radiation therapy
  • breast cancer risk