An epidemiological modeling and data integration framework.

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.

Demoting redundant features to improve the discriminatory ability in cancer data.

The identification of a set of relevant but not redundant features is an important first step in building predictive and diagnostic models from biomedical data sets. Most commonly, individual features are ranked in terms of a quality criterion, out of which the best (first) k features are selected. However, feature ranking methods do not sufficiently account for interactions and correlations between the features. Thus, redundancy is likely to be encountered in the selected features. We present a new algorithm, termed Redundancy Demoting (RD), that takes an arbitrary feature ranking as input, and improves this ranking by identifying redundant features and demoting them to positions in the ranking in which they are not redundant. Redundant features are those that are correlated with other features and not relevant in the sense that they do not improve the discriminatory ability of a set of features. Experiments on two cancer data sets, one melanoma image data set and one lung cancer microarray data set, show that our algorithm greatly improves the feature rankings provided by the methods information gain, ReliefF and Student's t-test in terms of predictive power.

A new ensemble-based algorithm for identifying breath gas marker candidates in liver disease using ion molecule reaction mass spectrometry.

MOTIVATION: Alcoholic fatty liver disease (AFLD) and non-AFLD (NAFLD) can progress to severe liver diseases such as steatohepatitis, cirrhosis and cancer. Thus, the detection of early liver disease is essential; however, minimal invasive diagnostic methods in clinical hepatology still lack specificity. RESULTS: Ion molecule reaction mass spectrometry (IMR-MS) was applied to a total of 126 human breath gas samples comprising 91 cases (AFLD, NAFLD and cirrhosis) and 35 healthy controls. A new feature selection modality termed Stacked Feature Ranking (SFR) was developed to identify potential liver disease marker candidates in breath gas samples, relying on the combination of different entropy- and correlation-based feature ranking methods including statistical hypothesis testing using a two-level architecture with a suggestion and a decision layer. We benchmarked SFR against four single feature selection methods, a wrapper and a recently described ensemble method, indicating a significantly higher discriminatory ability of up to 10-15% for the SFR selected gas compounds expressed by the area under the ROC curve (AUC) of 0.85-0.95. Using this approach, we were able to identify unexpected breath gas marker candidates in liver disease of high predictive value. A literature study further supports top-ranked markers to be associated with liver disease. We propose SFR as a powerful tool for biomarker search in breath gas and other biological samples using mass spectrometry. AVAILABILITY: The algorithm SFR and IMR-MS datasets are available under http://biomed.umit.at/page.cfm?pageid=526.