Quantifying and comparing the predictive accuracy of continuous prognostic factors for binary outcomes

The positive and negative predictive values are standard ways of quantifying predictive accuracy when both the outcome and the prognostic factor are binary. Methods for comparing the predictive values of two or more binary factors have been discussed previously (Leisenring et al., 2000, Biometrics 56, 345-351). We propose extending the standard definitions of the predictive values to accommodate prognostic factors that are measured on a continuous scale and suggest a corresponding graphical method to summarize predictive accuracy. Drawing on the work of Leisenring et al. we make use of a marginal regression framework and discuss methods for estimating these predictive value functions and their differences within this framework. The methods presented in this paper have the potential to be useful in a number of areas including the design of clinical trials and health policy analysis.     

Distribution-free ROC analysis using binary regression techniques

Receiver operating characteristic (ROC) regression methodology is used to identify factors that affect the accuracy of medical diagnostic tests. In this paper, we consider a ROC model for which the ROC curve is a parametric function of covariates but distributions of the diagnostic test results are not specified. Covariates can be either common to all subjects or specific to those with disease. We propose a new estimation procedure based on binary indicators defined by the test result for a diseased subject exceeding various specified quantiles of the distribution of test results from non-diseased subjects with the same covariate values. This procedure is conceptually and computationally simplified relative to existing procedures. Simulation study results indicate that the approach has fairly high statistical efficiency. The new ROC regression methodology is used to evaluate childhood measurements of body mass index as a predictive marker of adult obesity.     

The analysis of placement values for evaluating discriminatory measures

The idea of using measurements such as biomarkers, clinical data, or molecular biology assays for classification and prediction is popular in modern medicine. The scientific evaluation of such measures includes assessing the accuracy with which they predict the outcome of interest. Receiver operating characteristic curves are commonly used for evaluating the accuracy of diagnostic tests. They can be applied more broadly, indeed to any problem involving classification to two states or populations (D= 0 or 1). We show that the ROC curve can be interpreted as a cumulative distribution function for the discriminatory measure Y in the affected population (D= 1) after Y has been standardized to the distribution in the reference population (D= 0). The standardized values are called placement values. If the placement values have a uniform(0, 1) distribution, then Y is not discriminatory, because its distribution in the affected population is the same as that in the reference population. The degree to which the distribution of the standardized measure differs from uniform(0, 1) is a natural way to characterize the discriminatory capacity of Y and provides a nontraditional interpretation for the ROC curve. Statistical methods for making inference about distribution functions therefore motivate new approaches to making inference about ROC curves. We demonstrate this by considering the ROC-GLM regression model and observing that it is equivalent to a regression model for the distribution of placement values. The likelihood of the placement values provides a new approach to ROC parameter estimation that appears to be more efficient than previously proposed methods. The method is applied to evaluate a pulmonary function measure in cystic fibrosis patients as a predictor of future occurrence of severe acute pulmonary infection requiring hospitalization. Finally, we note the relationship between regression models for the mean placement value and recently proposed models for the area under the ROC curve which is the classic summary index of discrimination.     

Genomics,Proteomics & Bioinformatics Volume 12 2014 CONTENTS

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Application of the “-Omic-” technologies in phytomedicine

The proof of efficacy of phytopreparations and the determination of their mode of action are permanent challenges for an evidence-based phytotherapy. The technology platform of genomics, proteomics and metabolomics ("-omic-" technologies) are high-throughput technologies. They increase substantially the number of proteins/genes that can be detected simultaneously and have the potential to relate complex mixtures to complex effects in the form of gene/protein expression profiles. Provided that phytopreparation-specific signatures in the form of gene/protein expression profiles can be developed, these technologies will be useful for the chemical and pharmacological standardization and the proof of the toxicological potential of a plant extract. Over a long-term perspective they may economize the proof of efficacy, the determination of the mode of action of phytomedicines and allow to investigate herbal extracts without prominent active principle(s). The application of this genomics revealed already that gene expression profiles induced by single drugs and the ones induced by the combination of the same drugs can be entirely different. These results make the information of the mode of action of isolated "active principles/lead substances" of phytopreparations questionable. The application of the "-omic-" technologies may lead to a change of paradigms towards the application of complex mixtures in medicine and open the new field of phytogenomics, -proteomics and -metabolomics.     

Systems Biology in Aging: Linking the Old and the Young

Aging can be defined as a process of progressive decline in the physiological capacity of an organism, manifested by accumulated alteration and destabilization at the whole system level. Systems biology approaches offer a promising new perspective to examine the old problem of aging. We begin this review by introducing the concepts of systems biology, and then illustrate the application of systems biology approaches to aging research, from gene expression profiling to network analysis. We then introduce the network that can be constructed using known lifespan and aging regulators, and conclude with a look forward to the future of systems biology in aging research. In summary, systems biology is not only a young field that may help us understand aging at a higher level, but also an important platform that can link different levels of knowledge on aging, moving us closer to a more comprehensive control of systematic decline during aging.     

Predicting protein three-dimensional structure

The current state of the art in modeling protein structure has been assessed, based on the results of the CASP (Critical Assessment of protein Structure Prediction) experiments. In comparative modeling, improvements have been made in sequence alignment, sidechain orientation and loop building. Refinement of the models remains a serious challenge. Improved sequence profile methods have had a large impact in fold recognition. Although there has been some progress in alignment quality, this factor still limits model usefulness. In ab initio structure prediction, there has been notable progress in building approximately correct structures of 40-60 residue-long protein fragments. There is still a long way to go before the general ab initio prediction problem is solved. Overall, the field is maturing into a practical technology, able to deliver useful models for a large number of sequences.     

Lost in the world of functional genomics, systems biology, and translational research: is there life after the Milstein award?

We have always wanted to save the world from the scourges of virus infection by developing better drugs and vaccines. But fully understanding the intricacies of virus–host interactions, the first step in achieving this goal, requires the ability to view the process on a grand scale. The advent of high-throughput technologies, such as DNA microarrays and mass spectrometry, provided the first opportunities to obtain such a view. Here, we describe our efforts to use these tools to focus on the changes in cellular gene expression and protein abundance that occur in response to virus infection. By examining these changes in a comprehensive manner, we have been able to discover exciting new insights into innate immunity, interferon and cytokine signaling, and the strategies used by viruses to overcome these cellular defenses. Functional genomics may yet save the world from killer viruses.     

Using cell engineering and omic tools for the improvement of cell culture processes

Significant strides have been made in mammalian cell based biopharmaceutical process and cell line development over the past years. With several established mammalian host cell lines and expression systems, optimization of selection systems to reduce development times and improvement of glycosylation patterns are only some of the advances being made to improve cell culture processes. In this article, the advances pertaining to cell line development and cell engineering strategies are discussed. An overview of the cell engineering strategies to enhance cellular characteristics by genetic manipulation are illustrated, focusing on the use of genomics and proteomics tools and their application in such endeavors. Included in this review are some of the early studies using the ‘omic’ technique to understand cellular mechanisms of product synthesis and secretion, apoptosis, cell proliferation and the influence of the physicochemical environment. The article highlights the significance of integrating genomics and proteomics data with the vast amounts of bioprocess data for improved analysis of the biological pathways involved. Further improvements of the techniques and methodologies used are needed but ultimately, the new cell engineering strategies should provide great insight into the regulatory networks within the cell in a bioprocess environment and how to manipulate them to increase overall productivity.     

Understanding aerobic/anaerobic metabolism in Caldibacillus debilis through a comparison with model organisms

Caldibacillus debilis GB1 is a facultative anaerobe isolated from a thermophilic aero-tolerant cellulolytic enrichment culture. There is a lack of representative proteomes of facultative anaerobic thermophilic Bacillaceae, exploring aerobic/anaerobic expression. The C. debilis GB1 genome was sequenced and annotated, and the proteome characterized under aerobic and anaerobic conditions while grown on cellobiose. The draft sequence of C. debilis GB1 contains a 3,340,752 bp chromosome and a 5,386 bp plasmid distributed over 49 contigs. Two-dimensional liquid chromatography mass spectrometry/mass spectrometry was used with Isobaric Tags for Relative and Absolute Quantification (iTRAQ) to compare protein expression profiles, focusing on energy production and conversion pathways. Under aerobic conditions, proteins in glycolysis and pyruvate fermentation pathways were down-regulated. Simultaneously, proteins within the tricarboxylic acid cycle, pyruvate dehydrogenase, the electron transport chain, and oxygen scavenging pathways showed increased amounts. Under anaerobic conditions, protein levels in fermentation pathways were consistent with the generated end-products: formate, acetate, ethanol, lactate, and CO₂. Under aerobic conditions CO₂ and acetate production was consistent with incomplete respiration. Through a direct comparison with gene expression profiles from Escherichia coli, we show that global regulation of core metabolism pathways is similar in thermophilic and mesophilic facultative anaerobes of the Phylum Proteobacteria and Firmicutes.