Human Genetics

, Volume 134, Issue 5, pp 479–495 | Cite as

An argument for mechanism-based statistical inference in cancer

  • Donald GemanEmail author
  • Michael Ochs
  • Nathan D. Price
  • Cristian Tomasetti
  • Laurent Younes
Review Paper
Part of the following topical collections:
  1. Computational Molecular Medicine


Cancer is perhaps the prototypical systems disease, and as such has been the focus of extensive study in quantitative systems biology. However, translating these programs into personalized clinical care remains elusive and incomplete. In this perspective, we argue that realizing this agenda—in particular, predicting disease phenotypes, progression and treatment response for individuals—requires going well beyond standard computational and bioinformatics tools and algorithms. It entails designing global mathematical models over network-scale configurations of genomic states and molecular concentrations, and learning the model parameters from limited available samples of high-dimensional and integrative omics data. As such, any plausible design should accommodate: biological mechanism, necessary for both feasible learning and interpretable decision making; stochasticity, to deal with uncertainty and observed variation at many scales; and a capacity for statistical inference at the patient level. This program, which requires a close, sustained collaboration between mathematicians and biologists, is illustrated in several contexts, including learning biomarkers, metabolism, cell signaling, network inference and tumorigenesis.


Decision Rule Metabolic Network Statistical Learning Prediction Rule Omics Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The work of D. Geman and L. Younes was partially supported by the National Science Foundation under NSF DMS1228248. N. Price’s work was supported by a Camille Dreyfus Teacher-Scholar Award and NIH 2P50GM076547.

Author contributions

D.G. supervised the project. All authors wrote the manuscript.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Donald Geman
    • 1
    Email author
  • Michael Ochs
    • 2
  • Nathan D. Price
    • 3
  • Cristian Tomasetti
    • 4
  • Laurent Younes
    • 1
  1. 1.Department of Applied Mathematics and StatisticsJohns Hopkins UniversityBaltimoreUSA
  2. 2.Mathematics and StatisticsThe College of New JerseyEwing TownshipUSA
  3. 3.Institute for Systems BiologySeattleUSA
  4. 4.Division of Biostatistics and Bioinformatics, and Department of BiostatisticsJohns Hopkins UniversityBaltimoreUSA

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