Springer Behaviormetrika special issue on "Probabilistic Graphical Models and its Applications to Biomedical Informatics"
Recent advances in low-cost, high-throughput measurement technologies, such as RNA-seq, have brought Big Data to the world of health care. Machine learning and artificial intelligence approaches are among the most promising techniques for extracting useful biological signals from the noise inherent in these technologies. Integrating such data with traditional medical data poses even more challenges. Nevertheless, this field of research is poised to offer effective, personalized medical treatment. This special issue focuses on probabilistic graphical modeling approaches to biomedical informatics. A wide variety of problems remain open in this domain. For example, regulatory and interaction networks can naturally be expressed as Bayesian or Markov networks; however, integrating heterogeneous data types, such as single nucleotide polymorphism information and RNA-seq, is often approached in an ad hoc, problem-dependent manner. Biological signals are known to exhibit complex, time-dependent and often non-acyclic dependencies; rigorously modeling such dynamic processes remains an open challenge. Furthermore, many biological problems suffer from “large p, small n,” in which many more variables (such as genes) are present than the number of samples. These settings remaining challenging for typical machine learning approaches. Many difficulties remain in medical informatics, as well. For example, many hospitals maintain databases of patient information; however, the information for individual patients is typically sparse, and sometimes even incorrect. Extracting useful knowledge from such unstructured data sources often requires probabilistic processing techniques. Similarly, many health care professionals do not have experience interpreting machine learning results. Thus, information retrieval and visualization techniques are also relevant, open questions for biomedical informatics. Topics that will be considered for this special issue include, but are not limited to, the following: - Learning Bayesian or Markov network structures for biomedical applications - Learning large-scale graphical models in a sparse setting - Computational efficiency of large-scale graphical model learning - Estimating network parameters for biomedical applications - Causal discovery and inference for biomedical applications - Mathematical modeling of high-throughput sequencing data, such as RNA-seq - Probabilistic approaches to gene differential analysis - Natural language processing for biomedical applications - Graphical visualization for biomedical applications - Probabilistic information retrieval techniques for biomedical applications http://www.springer.com/?SGWID=0-102-2-1544144-preview&dynamic=true [Important Dates] Submission open: May 1 2016 Submission deadline: August 15 2016 Final acceptance: November 15 2016 Expected publication: January 2017 [Contact] Editors for this special issue: Joe Suzuki (suz...@math.sci.osaka-u.ac.jp); Brandon Malone (brandon.mal...@uni-heidelberg.de) Advisor: Yutaka Kano Editor-in-Chief: Maomi Ueno _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
