Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery

C. F. Aliferis, A. R. Statnikov, I. Tsamardinos, L. E. Brown

Research output: Chapter in Book/Report/Conference proceedingConference contribution

70 Scopus citations

Abstract

Causal Probabilistic Networks (CPNs), (a.k.a. Bayesian Networks, or Belief Networks) are well-established representations in biomedical applications such as decision support systems and predictive modeling or mining of causal hypotheses. CPNs (a) have well-developed theory for induction of causal relationships, and (b) are suitable for creating sound and practical decision support systems. While several public domain and commercial tools exist for modeling and inference with CPNs, very few software tools and libraries exist currently that give access to algorithms for CPN induction. To that end, we have developed a software library, called Causal Explorer, that implements a suit of global, local and partial CPN induction algorithms. The toolkit emphasizes causal discovery algorithms. Approximately half of the algorithms are enhanced implementations of well-established algorithms, and the remaining ones are novel local and partial algorithms that scale to thousands of variables and thus are particularly suitable for modeling in massive datasets.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS 03
EditorsF. Valafar, H. Valafar
Pages371-376
Number of pages6
StatePublished - Dec 1 2003
EventProceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS'03 - Las Vegas, NV, United States
Duration: Jun 23 2003Jun 26 2003

Publication series

NameProceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences

Other

OtherProceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS'03
CountryUnited States
CityLas Vegas, NV
Period6/23/036/26/03

Keywords

  • Bayesian Models in Medicine
  • Data Mining and Bioinformatics
  • Software Tools for Bioinformatics Community

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