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Summary Objectives: In this work, a cellular automaton software package for simulating different infectious diseases, storing the simulation results in a data warehouse system and analyzing the obtained results to generate prediction models as well as contingency plans, is proposed. The Brisbane H3N2 flu virus, which has been spreading during the winter season 2009, was used for simulation in the federal state of Tyrol, Austria. Methods: The simulation-modeling framework consists of an underlying cellular automaton. The cellular automaton model is parameterized by known disease parameters and geographical as well as demographical conditions are included for simulating the spreading. The data generated by simulation are stored in the back room of the data warehouse using the Talend Open Studio software package, and subsequent statistical and data mining tasks are performed using the tool, termed Knowledge Discovery in Database Designer (KD3). Results: The obtained simulation results were used for generating prediction models for all nine federal states of Austria. Conclusion: The proposed framework provides a powerful and easy to handle interface for parameterizing and simulating different infectious diseases in order to generate prediction models and improve contingency plans for future events.

Motivation: Prostate cancer is the most prevalent tumor in males and its incidence is expected to increase as the population ages. Prostate cancer is treatable by excision if detected at an early enough stage. The challenges of early diagnosis require the discovery of novel biomarkers and tools for prostate cancer management. Results: We developed a novel feature selection algorithm termed as associative voting (AV) for identifying biomarker candidates in prostate cancer data measured via targeted metabolite profiling MS/MS analysis. We benchmarked our algorithm against two standard entropy-based and correlation-based feature selection methods [Information Gain (IG) and ReliefF (RF)] and observed that, on a variety of classification tasks in prostate cancer diagnosis, our algorithm identified subsets of biomarker candidates that are both smaller and show higher discriminatory power than the subsets identified by IG and RF. A literature study confirms that the highest ranked biomarker candidates identified by AV have independently been identified as important factors in prostate cancer development. Availability: The algorithm can be downloaded from the following http://biomed.umit.at/page.cfm?pageid=516 Contact: melanie.osl@umit.at

In this paper, a cellular automaton framework for processing the spatiotemporal spread of infectious diseases is presented. The developed environment simulates and visualizes how infectious diseases might spread, and hence provides a powerful instrument for health care organizations to generate disease prevention and contingency plans. In this study, the outbreak of an avian flu like virus was modeled in the state of Tyrol, and various scenarios such as quarantine, effect of different medications on viral spread and changes of social behavior were simulated. The proposed framework is implemented using the programming language Java. The set up of the simulation environment requires specification of the disease parameters and the geographical information using a population density colored map, enriched with demographic data. The results of the numerical simulations and the analysis of the computed parameters will be used to get a deeper understanding of how the disease spreading mechanisms work, and how to protect the population from contracting the disease. Strategies for optimization of medical treatment and vaccination regimens will also be investigated using our cellular automaton framework. In this study, six different scenarios were simulated. It showed that geographical barriers may help to slow down the spread of an infectious disease, however, when an aggressive and deadly communicable disease spreads, only quarantine and controlled medical treatment are able to stop the outbreak, if at all.

Medical diagnosis and prognosis using machine learning methods is usually represented as a supervised classification problem, where a model is built to distinguish "normal" from "abnormal" cases. If cases are available from only one class, this approach is not feasible.To evaluate the performance of classification via outlier detection by one-class support vector machines (SVMs) as a means of identifying abnormal cases in the domain of melanoma prognosis.Empirical evaluation of one-class SVMs on a data set for predicting the presence or absence of metastases in melanoma patients, and comparison with regular SVMs and artificial neural networks.One-class SVMs achieve an area under the ROC curve (AUC) of 0.71; two-class algorithms achieve AUCs between 0.5 and 0.84, depending on the available number of cases from the minority class.One-class SVMs offer a viable alternative to two-class classification algorithms if class distribution is heavily imbalanced.

Objective: To evaluate and compare the performance of different rule-ranking algorithms for rule-based classifiers on biomedical datasets. Methodology: Empirical evaluation of five rule ranking algorithms on two biomedical datasets, with performance evaluation based on ROC analysis and 5x2 cross-validation. Results: On a lung cancer dataset, the area under the ROC curve (AUC) of, on average, 14267.1 rules was 0.862. Multi-rule ranking found 13.3 rules with an AUC of 0.852. Four single-rule ranking algorithms, using the same number of rules, achieved average AUC values of 0.830, 0.823, 0.823, and 0.822, respectively. On a prostate cancer dataset, an average of 339265.3 rules had an AUC of 0.934, while 9.4 rules obtained from multi-rule and single-rule rankings had average AUCs of 0.932, 0.926, 0.925, 0.902 and 0.902, respectively. Conclusion: Multi-variate rule ranking performs better than the single-rule ranking algorithms. Both single-rule and multi-rule methods are able to substantially reduce the number of rules while keeping classification performance at a level comparable to the full rule set.

Classifiers based on parametric or non-parametric learning methods have different advantages and disadvantages. To take advantage of the strengths of both methods, we propose an algorithm that combines a parametric model (logistic regression) with a non-parametric classification method (k-nearest neighbors). This combination is based on a measure of appropriateness that uses a heuristic to decide which of the two components should contribute more to the final classification output. We measure the performance of this combination method on two data sets (one from medical informatics, and one consisting of simulated data) in terms of areas under the ROC curves (AUCs). We are able to demonstrate that our method of combining classifiers exceeds the performance of both individual classifiers taken separately.

A core technology in proteomics is mass spectroscopy (MS) that permits the measurement of thousands of proteins/peptides simultanously. Sophisticated data mining methods are necessary to identify highly predictive proteomic biomarker candidates in generated MS spectra that are specific to a certain disease. However, before analysis can be started the preprocessing of raw mass spectra is an essential task, mainly due to the presence of background signals in the spectra such as electrical and chemical noise. In this work we present a new data mining workflow for the identification of proteomic biomarker candidates using mass spectrometry data. The workflow includes two major steps: 1) the preprocessing of raw spectra, and 2) the identification of highly discriminating candidate masses using a 3-step feature selection approach by combining the advantages of efficient filter and effective wrapper techniques. With the proposed workflow we were able to identify putative candidate biomarkers in a lifethreatening human disease using matrix-assisted laser desorption/ionization imaging MS (MALDI-IMS).

The search and validation of novel disease biomarkers requires the complementary power of professional study planning and execution, modern profiling technologies and related bioinformatics tools for data analysis and interpretation. Biomarkers have considerable impact on the care of patients and are urgently needed for advancing diagnostics, prognostics and treatment of disease. This survey article highlights emerging bioinformatics methods for biomarker discovery in clinical metabolomics, focusing on the problem of data preprocessing and consolidation, the data-driven search, verification, prioritization and biological interpretation of putative metabolic candidate biomarkers in disease. In particular, data mining tools suitable for the application to omic data gathered from most frequently-used type of experimental designs, such as case-control or longitudinal biomarker cohort studies, are reviewed and case examples of selected discovery steps are delineated in more detail. This review demonstrates that clinical bioinformatics has evolved into an essential element of biomarker discovery, translating new innovations and successes in profiling technologies and bioinformatics to clinical application.