Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Theory refinement on Bayesian networks
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning - Special issue on learning with probabilistic representations
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A Pretopological Approach for Image Segmentation and Edge Detection
Journal of Mathematical Imaging and Vision
Shape Connectivity: Multiscale Analysis and Application to Generalized Granulometries
Journal of Mathematical Imaging and Vision
A Guide to the Literature on Learning Probabilistic Networks from Data
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Learning Bayesian networks with local structure
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
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Ideally biosignatures can be detected at the early infection phase and used both for developing diagnostic patterns and for prognostic triage. Such biosignatures are important for vaccine validation and to provide risk stratification to a population such as for the identification of individuals who are exposed to biological or chemical agents and who are at high risk for developing an infection. The research goal is to detect broad based biosignature models and is initially focused on developing effective computer-augmented pathology tied to animal models developed at the University of New Mexico (UNM). Using lung tissue from infected and naı¨ve mice, feature extraction from images of the tissue under a specialized microscope, and Bayesian networks to analyze the data sets of features, we were able to differentiate normal from diseased samples and viral from bacterial samples in mid to late stages of infection. This effort has shown the potential effectiveness of computer-augmented pathology in this application. The extended research intends to couple analysis of serum, microarray analysis of organs, proteomic data and the pathology. The rational for the current invasive procedure on animal models is to facilitate the development of data analysis and machine learning techniques that can eventually be generalized to the task of discovering non-invasive and early stage biosignatures for human models.