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Big Data Approaches for Modeling Response and Resistance to Cancer Drugs

Peng Jiang1, William R Sellers2, X Shirley Liu1

  • 1Dana-Farber Cancer Institute and Harvard T.H. Chan School of Public Health, Boston, Massachusetts 02215, USA.

Annual Review of Biomedical Data Science
|July 26, 2019
PubMed

Insights

Data-driven approaches are revolutionizing cancer research by analyzing big data and genomics to improve anticancer drug efficacy. This shift offers new opportunities for personalized cancer therapeutics and biomarker discovery.

Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Standard cancer treatments are insufficient for many patients, necessitating novel therapeutic strategies.
  • Hypothesis-driven research traditionally guides cancer biology, but big data analytics are emerging as a powerful complementary approach.
  • High-throughput technologies have fueled an explosion of data, enabling new computational methods in cancer research.

Purpose of the Study:

  • To review recent advancements in data-driven methodologies for predicting anticancer drug efficacy.
  • To highlight the challenges and future opportunities for data science in advancing cancer therapeutics.

Main Methods:

  • Focus on computational methods and genomics data integration.
  • Analysis of big data resources generated by high-throughput technologies.
  • Review of recent literature on data-driven modeling of drug efficacy.

Main Results:

  • Data-driven methods combined with genomics show promise for clinical applications in cancer therapy.
  • Significant progress has been made in modeling anticancer drug efficacy using computational approaches.
  • The review identifies key challenges and opportunities in applying data science to cancer therapeutics.

Conclusions:

  • Data-driven research represents a paradigm shift in cancer therapeutic discovery.
  • Computational methods and big data analytics are crucial for identifying better predictive biomarkers and treatment regimes.
  • Further integration of data science holds significant potential for improving cancer patient outcomes.
Keywords:
big datacombination therapydrug resistanceimmunotherapyprecision medicineresponse biomarkertoxicity

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