From big data to smart data in Alzheimer's disease. The brain health modeling initiative to foster actionable knowledge

Hugo Geerts, Penny A. Dacks, Viswanath Devanarayan, Magali Haas, Zaven Khachaturian, Mark Forrest Gordon, Stuart Maudsley, Klaus Romero, Diane Stephenson

Research output: Contribution to journalArticlepeer-review

44 Scopus citations


Massive investment and technological advances in the collection of extensive and longitudinal information on thousands of Alzheimer patients results in large amounts of data. These "big-data" databases can potentially advance CNS research and drug development. However, although necessary, they are not sufficient, and we posit that they must be matched with analytical methods that go beyond retrospective data-driven associations with various clinical phenotypes. Although these empirically derived associations can generate novel and useful hypotheses, they need to be organically integrated in a quantitative understanding of the pathology that can be actionable for drug discovery and development. We argue that mechanism-based modeling and simulation approaches, where existing domain knowledge is formally integrated using complexity science and quantitative systems pharmacology can be combined with data-driven analytics to generate predictive actionable knowledge for drug discovery programs, target validation, and optimization of clinical development.

Original languageEnglish (US)
JournalAlzheimer's and Dementia
StateAccepted/In press - 2016
Externally publishedYes


  • Alzheimer's dementia
  • Brain disorders
  • Complexity theory
  • Drug discovery and development
  • Systems biology
  • Systems pharmacology

ASJC Scopus subject areas

  • Clinical Neurology
  • Developmental Neuroscience
  • Cellular and Molecular Neuroscience
  • Psychiatry and Mental health
  • Geriatrics and Gerontology
  • Epidemiology
  • Health Policy


Dive into the research topics of 'From big data to smart data in Alzheimer's disease. The brain health modeling initiative to foster actionable knowledge'. Together they form a unique fingerprint.

Cite this