Interpreter of maladies: Redescription mining applied to biomedical data analysis

Peter Waltman, Alex Pearlman, Bud Mishra

Research output: Contribution to journalArticlepeer-review


Comprehensive, systematic and integrated data-centric statistical approaches to disease modeling can provide powerful frameworks for understanding disease etiology. Here, one such computational framework based on redescription mining in both its incarnations, static and dynamic, is discussed. The static framework provides bioinformatic tools applicable to multifaceted datasets, containing genetic, transcriptomic, proteomic, and clinical data for diseased patients and normal subjects. The dynamic redescription framework provides systems biology tools to model complex sets of regulatory, metabolic and signaling pathways in the initiation and progression of a disease. As an example, the case of chronic fatigue syndrome (CFS) is considered, which has so far remained intractable and unpredictable in its etiology and nosology. The redescription mining approaches can be applied to the Centers for Disease Control and Prevention's Wichita (KS, USA) dataset, integrating transcriptomic, epidemiological and clinical data, and can also be used to study how pathwa ys in the hypothalamic-pituitary-adrenal axis affect CFS patients.

Original languageEnglish (US)
Pages (from-to)503-509
Number of pages7
Issue number3
StatePublished - Apr 2006


  • Chronic fatigue syndrome
  • Redescription analysis
  • Statistical analysis

ASJC Scopus subject areas

  • Molecular Medicine
  • Genetics
  • Pharmacology


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