MALDI-MS tissue imaging identification of biliverdin reductase B overexpression in prostate cancer.

New biomarkers are needed to improve the specificity of prostate cancer detection and characterisation of individual tumors. In a proteomics profiling approach using MALDI-MS tissue imaging on frozen tissue sections, we identified discriminating masses. Imaging analysis of cancer, non-malignant benign epithelium and stromal areas of 15 prostatectomy specimens in a test and 10 in a validation set identified characteristic m/z peaks for each tissue type, e.g. m/z 10775 for benign epithelial, m/z 6284 and m/z 6657.5 for cancer and m/z 4965 for stromal tissue. A 10-fold cross-validation analysis showed highest discriminatory ability to separate tissue types for m/z 6284 and m/z 6657.5, both overexpressed in cancer, and a multicomponent mass peak cluster at m/z 10775-10797.4 overexpressed in benign epithelial tissue. ROC AUC values for these three masses ranged from 0.85 to 0.95 in the discrimination of malignant and non-malignant tissue. To identify the underlying proteins, prostate whole tissue extract was separated by nano-HPLC and subjected to MALDI TOF/TOF analysis. Proteins in fractions containing discriminatory m/z masses were identified by MS/MS analysis and candidate marker proteins subsequently validated by immunohistochemistry (IHC). Biliverdin reductase B (BLVRB) turned out to be overexpressed in PCa tissue. BIOLOGICAL SIGNIFICANCE: In this study on cryosections of radical prostatectomies of prostate cancer patients, we performed a MALDI-MS tissue imaging analysis and a consecutive protein identification of significant m/z masses by nano-HPLC, MALDI TOF/TOF and MS/MS analysis. We identified BLVRB as a potential biomarker in the discrimination of PCa and benign tissue, also suggesting BVR as a feasible therapeutic target.

Calibrating predictive model estimates to support personalized medicine.

OBJECTIVE: Predictive models that generate individualized estimates for medically relevant outcomes are playing increasing roles in clinical care and translational research. However, current methods for calibrating these estimates lose valuable information. Our goal is to develop a new calibration method to conserve as much information as possible, and would compare favorably to existing methods in terms of important performance measures: discrimination and calibration. MATERIAL AND METHODS: We propose an adaptive technique that utilizes individualized confidence intervals (CIs) to calibrate predictions. We evaluate this new method, adaptive calibration of predictions (ACP), in artificial and real-world medical classification problems, in terms of areas under the ROC curves, the Hosmer-Lemeshow goodness-of-fit test, mean squared error, and computational complexity. RESULTS: ACP compared favorably to other calibration methods such as binning, Platt scaling, and isotonic regression. In several experiments, binning, isotonic regression, and Platt scaling failed to improve the calibration of a logistic regression model, whereas ACP consistently improved the calibration while maintaining the same discrimination or even improving it in some experiments. In addition, the ACP algorithm is not computationally expensive. LIMITATIONS: The calculation of CIs for individual predictions may be cumbersome for certain predictive models. ACP is not completely parameter-free: the length of the CI employed may affect its results. CONCLUSIONS: ACP can generate estimates that may be more suitable for individualized predictions than estimates that are calibrated using existing methods. Further studies are necessary to explore the limitations of ACP.

Testing the calibration of classification models from first principles.

The accurate assessment of the calibration of classification models is severely limited by the fact that there is no easily available gold standard against which to compare a model's outputs. The usual procedures group expected and observed probabilities, and then perform a χ(2) goodness-of-fit test. We propose an entirely new approach to calibration testing that can be derived directly from the first principles of statistical hypothesis testing. The null hypothesis is that the model outputs are correct, i.e., that they are good estimates of the true unknown class membership probabilities. Our test calculates a p-value by checking how (im)probable the observed class labels are under the null hypothesis. We demonstrate by experiments that our proposed test performs comparable to, and sometimes even better than, the Hosmer-Lemeshow goodness-of-fit test, the de facto standard in calibration assessment.

Bioinformatic-driven search for metabolic biomarkers in disease.

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.

Smooth isotonic regression: a new method to calibrate predictive models.

Predictive models are critical for risk adjustment in clinical research. Evaluation of supervised learning models often focuses on predictive model discrimination, sometimes neglecting the assessment of their calibration. Recent research in machine learning has shown the benefits of calibrating predictive models, which becomes especially important when probability estimates are used for clinical decision making. By extending the isotonic regression method for recalibration to obtain a smoother fit in reliability diagrams, we introduce a novel method that combines parametric and non-parametric approaches. The method calibrates probabilistic outputs smoothly and shows better generalization ability than its ancestors in simulated as well as real world biomedical data sets.

An evaluation of heuristics for rule ranking.

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 5 × 2 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.

Effect of data combination on predictive modeling: a study using gene expression data.

BACKGROUND: The quality of predictive modeling in biomedicine depends on the amount of data available for model building. OBJECTIVE: To study the effect of combining microarray data sets on feature selection and predictive modeling performance. METHODS: Empirical evaluation of stability of feature selection and discriminatory power of classifiers using three previously published gene expression data sets, analyzed both individually and in combination. RESULTS: Feature selection was not robust for the individual as well as for the combined data sets. The classification performance of models built on individual and combined data sets was heavily dependent on the data set from which the features were extracted. CONCLUSION: We identified volatility of feature selection as contributing factor to some of the problems faced by predictive modeling using microarray data.

Outlier Detection with One-Class SVMs: An Application to Melanoma Prognosis.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: One-class SVMs offer a viable alternative to two-class classification algorithms if class distribution is heavily imbalanced.

Improving calibration of logistic regression models by local estimates.

OBJECTIVE: To improve the calibration of logistic regression (LR) estimates using local information. BACKGROUND: Individualized risk assessment tools are increasingly being utilized. External validation of these tools often reveals poor model calibration. METHODS: We combine a clustering algorithm with an LR model to produce probability estimates that are close to the true probabilities for a particular case. The new method is compared to a standard LR model in terms of calibration, as measured by the sum of absolute differences (SAD) between model estimates and true probabilities, and discrimination, as measured by area under the ROC curve (AUC). RESULTS: We evaluate the new method on two synthetic data sets. SADs are significantly lower (p < 0.0001) in both data sets, and AUCs are significantly higher in one data set (p < 0.01). CONCLUSION: The results suggest that the proposed method may be useful to improve the calibration of LR models.

A new rule-based algorithm for identifying metabolic markers in prostate cancer using tandem mass spectrometry.

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.

A cellular automaton framework for infectious disease spread simulation.

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.