A link prediction approach to cancer drug sensitivity prediction

Turki Turki, Zhi Wei

Research output: Contribution to journalArticlepeer-review

26 Scopus citations

Abstract

Background: Predicting the response to a drug for cancer disease patients based on genomic information is an important problem in modern clinical oncology. This problem occurs in part because many available drug sensitivity prediction algorithms do not consider better quality cancer cell lines and the adoption of new feature representations; both lead to the accurate prediction of drug responses. By predicting accurate drug responses to cancer, oncologists gain a more complete understanding of the effective treatments for each patient, which is a core goal in precision medicine. Results: In this paper, we model cancer drug sensitivity as a link prediction, which is shown to be an effective technique. We evaluate our proposed link prediction algorithms and compare them with an existing drug sensitivity prediction approach based on clinical trial data. The experimental results based on the clinical trial data show the stability of our link prediction algorithms, which yield the highest area under the ROC curve (AUC) and are statistically significant. Conclusions: We propose a link prediction approach to obtain new feature representation. Compared with an existing approach, the results show that incorporating the new feature representation to the link prediction algorithms has significantly improved the performance.

Original languageEnglish (US)
Article number94
JournalBMC Systems Biology
Volume11
DOIs
StatePublished - Oct 3 2017

All Science Journal Classification (ASJC) codes

  • Structural Biology
  • Modeling and Simulation
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

Keywords

  • Applications in biology and medicine
  • Cancer drug discovery
  • Feature learning
  • Link prediction
  • Precision medicine

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