@inproceedings{54a78b30f8db4f88b0f7e9bf0cdbd88b,

title = "Causal Explorer: A Causal Probabilistic Network Learning Toolkit for Biomedical Discovery",

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.",

keywords = "Bayesian Models in Medicine, Data Mining and Bioinformatics, Software Tools for Bioinformatics Community",

author = "Aliferis, {C. F.} and Statnikov, {A. R.} and I. Tsamardinos and Brown, {L. E.}",

year = "2003",

language = "English (US)",

isbn = "1932415041",

series = "Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences",

pages = "371--376",

editor = "F. Valafar and H. Valafar",

booktitle = "Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS 03",

note = "Proceedings of the International Conference on Mathematics and Engineering Techniques in Medicine and Biological Sciences, METMBS'03 ; Conference date: 23-06-2003 Through 26-06-2003",

}