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Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction

Author

Listed:
  • E Tejera
  • I Carrera
  • Karina Jimenes-Vargas
  • V Armijos-Jaramillo
  • A Sánchez-Rodríguez
  • M Cruz-Monteagudo
  • Y Perez-Castillo

Abstract

The prediction of cell-lines sensitivity to a given set of compounds is a very important factor in the optimization of in-vitro assays. To date, the most common prediction strategies are based upon machine learning or other quantitative structure-activity relationships (QSAR) based approaches. In the present research, we propose and discuss a straightforward strategy not based on any learning modelling but exclusively relying upon the chemical similarity of a query compound to reference compounds with annotated activity against cell lines. We also compare the performance of the proposed method to machine learning predictions on the same problem. A curated database of compounds-cell lines associations derived from ChemBL version 22 was created for algorithm construction and cross-validation. Validation was done using 10-fold cross-validation and testing the models on new data obtained from ChemBL version 25. In terms of accuracy, both methods perform similarly with values around 0.65 across 750 cell lines in 10-fold cross-validation experiments. By combining both methods it is possible to achieve 66% of correct classification rate in more than 26000 newly reported interactions comprising 11000 new compounds. A Web Service implementing the described approaches (both similarity and machine learning based models) is freely available at: http://bioquimio.udla.edu.ec/cellfishing.

Suggested Citation

  • E Tejera & I Carrera & Karina Jimenes-Vargas & V Armijos-Jaramillo & A Sánchez-Rodríguez & M Cruz-Monteagudo & Y Perez-Castillo, 2019. "Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-11, October.
  • Handle: RePEc:plo:pone00:0223276
    DOI: 10.1371/journal.pone.0223276
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    References listed on IDEAS

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    1. Naiqian Zhang & Haiyun Wang & Yun Fang & Jun Wang & Xiaoqi Zheng & X Shirley Liu, 2015. "Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model," PLOS Computational Biology, Public Library of Science, vol. 11(9), pages 1-18, September.
    2. Kejian Wang & Jiazhi Sun & Shufeng Zhou & Chunling Wan & Shengying Qin & Can Li & Lin He & Lun Yang, 2013. "Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-9, November.
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