An argument for mechanism-based statistical inference in cancer

Donald Geman, Michael Ochs, Nathan D. Price, Cristian Tomasetti, Laurent Younes

Research output: Contribution to journalReview article

Abstract

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.

Original languageEnglish (US)
Pages (from-to)479-495
Number of pages17
JournalHuman genetics
Volume134
Issue number5
DOIs
StatePublished - May 1 2015

ASJC Scopus subject areas

  • Genetics
  • Genetics(clinical)

Fingerprint Dive into the research topics of 'An argument for mechanism-based statistical inference in cancer'. Together they form a unique fingerprint.

  • Cite this