A boosting method for maximizing the partial area under the ROC curve

<p>Abstract</p> <p>Background</p> <p>The receiver operating characteristic (ROC) curve is a fundamental tool to assess the discriminant performance for not only a single marker but also a score function combining multiple markers. The area under the ROC curve (AUC) for a score function measures the intrinsic ability for the score function to discriminate between the controls and cases. Recently, the partial AUC (pAUC) has been paid more attention than the AUC, because a suitable range of the false positive rate can be focused according to various clinical situations. However, existing pAUC-based methods only handle a few markers and do not take nonlinear combination of markers into consideration.</p> <p>Results</p> <p>We have developed a new statistical method that focuses on the pAUC based on a boosting technique. The markers are combined componentially for maximizing the pAUC in the boosting algorithm using natural cubic splines or decision stumps (single-level decision trees), according to the values of markers (continuous or discrete). We show that the resulting score plots are useful for understanding how each marker is associated with the outcome variable. We compare the performance of the proposed boosting method with those of other existing methods, and demonstrate the utility using real data sets. As a result, we have much better discrimination performances in the sense of the pAUC in both simulation studies and real data analysis.</p> <p>Conclusions</p> <p>The proposed method addresses how to combine the markers after a pAUC-based filtering procedure in high dimensional setting. Hence, it provides a consistent way of analyzing data based on the pAUC from maker selection to marker combination for discrimination problems. The method can capture not only linear but also nonlinear association between the outcome variable and the markers, about which the nonlinearity is known to be necessary in general for the maximization of the pAUC. The method also puts importance on the accuracy of classification performance as well as interpretability of the association, by offering simple and smooth resultant score plots for each marker.</p

Spatially-resolved electronic and vibronic properties of single diamondoid molecules

Diamondoids are a unique form of carbon nanostructure best described as hydrogen-terminated diamond molecules. Their diamond-cage structures and tetrahedral sp3 hybrid bonding create new possibilities for tuning electronic band gaps, optical properties, thermal transport, and mechanical strength at the nanoscale. The recently-discovered higher diamondoids (each containing more than three diamond cells) have thus generated much excitement in regards to their potential versatility as nanoscale devices. Despite this excitement, however, very little is known about the properties of isolated diamondoids on metal surfaces, a very relevant system for molecular electronics. Here we report the first molecular scale study of individual tetramantane diamondoids on Au(111) using scanning tunneling microscopy and spectroscopy. We find that both the diamondoid electronic structure and electron-vibrational coupling exhibit unique spatial distributions characterized by pronounced line nodes across the molecular surfaces. Ab-initio pseudopotential density functional calculations reveal that the observed dominant electronic and vibronic properties of diamondoids are determined by surface hydrogen terminations, a feature having important implications for designing diamondoid-based molecular devices.Comment: 16 pages, 4 figures. to appear in Nature Material

A Unifying Framework for Evaluating the Predictive Power of Genetic Variants Based on the Level of Heritability Explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait

Wheat rusts never sleep but neither do sequencers: will pathogenomics transform the way plant diseases are managed?

Field pathogenomics adds highly informative data to surveillance surveys by enabling rapid evaluation of pathogen variability, population structure and host genotype

FcRn-mediated antibody transport across epithelial cells revealed by electron tomography

The neonatal Fc receptor (FcRn) transports maternal IgG across epithelial barriers, thereby providing the fetus or newborn with humoral immunity before its immune system is fully functional. In newborn rats, FcRn transfers IgG from milk to blood by apical-to-basolateral transcytosis across intestinal epithelial cells. The pH difference between the apical (pH 6.0–6.5) and basolateral (pH 7.4) sides of intestinal epithelial cells facilitates the efficient unidirectional transport of IgG, because FcRn binds IgG at pH 6.0–6.5 but not at pH 7 or more. As milk passes through the neonatal intestine, maternal IgG is removed by FcRn-expressing cells in the proximal small intestine (duodenum and jejunum); remaining proteins are absorbed and degraded by FcRn-negative cells in the distal small intestine (ileum). Here we use electron tomography to make jejunal transcytosis visible directly in space and time, developing new labelling and detection methods to map individual nanogold-labelled Fc within transport vesicles and simultaneously to characterize these vesicles by immunolabelling. Combining electron tomography with a nonperturbing endocytic label allowed us to conclusively identify receptor-bound ligands, resolve interconnecting vesicles, determine whether a vesicle was microtubule-associated, and accurately trace FcRn-mediated transport of IgG. Our results present a complex picture in which Fc moves through networks of entangled tubular and irregular vesicles, only some of which are microtubule-associated, as it migrates to the basolateral surface. New features of transcytosis are elucidated, including transport involving multivesicular body inner vesicles/tubules and exocytosis through clathrin-coated pits. Markers for early, late and recycling endosomes each labelled vesicles in different and overlapping morphological classes, revealing spatial complexity in endo-lysosomal trafficking

Qualia: The Geometry of Integrated Information

According to the integrated information theory, the quantity of consciousness is the amount of integrated information generated by a complex of elements, and the quality of experience is specified by the informational relationships it generates. This paper outlines a framework for characterizing the informational relationships generated by such systems. Qualia space (Q) is a space having an axis for each possible state (activity pattern) of a complex. Within Q, each submechanism specifies a point corresponding to a repertoire of system states. Arrows between repertoires in Q define informational relationships. Together, these arrows specify a quale—a shape that completely and univocally characterizes the quality of a conscious experience. Φ— the height of this shape—is the quantity of consciousness associated with the experience. Entanglement measures how irreducible informational relationships are to their component relationships, specifying concepts and modes. Several corollaries follow from these premises. The quale is determined by both the mechanism and state of the system. Thus, two different systems having identical activity patterns may generate different qualia. Conversely, the same quale may be generated by two systems that differ in both activity and connectivity. Both active and inactive elements specify a quale, but elements that are inactivated do not. Also, the activation of an element affects experience by changing the shape of the quale. The subdivision of experience into modalities and submodalities corresponds to subshapes in Q. In principle, different aspects of experience may be classified as different shapes in Q, and the similarity between experiences reduces to similarities between shapes. Finally, specific qualities, such as the “redness” of red, while generated by a local mechanism, cannot be reduced to it, but require considering the entire quale. Ultimately, the present framework may offer a principled way for translating qualitative properties of experience into mathematics

Systematic Evaluation of Candidate Blood Markers for Detecting Ovarian Cancer

Epithelial ovarian cancer is a significant cause of mortality both in the United States and worldwide, due largely to the high proportion of cases that present at a late stage, when survival is extremely poor. Early detection of epithelial ovarian cancer, and of the serous subtype in particular, is a promising strategy for saving lives. The low prevalence of ovarian cancer makes the development of an adequately sensitive and specific test based on blood markers very challenging. We evaluated the performance of a set of candidate blood markers and combinations of these markers in detecting serous ovarian cancer.We selected 14 candidate blood markers of serous ovarian cancer for which assays were available to measure their levels in serum or plasma, based on our analysis of global gene expression data and on literature searches. We evaluated the performance of these candidate markers individually and in combination by measuring them in overlapping sets of serum (or plasma) samples from women with clinically detectable ovarian cancer and women without ovarian cancer. Based on sensitivity at high specificity, we determined that 4 of the 14 candidate markers--MUC16, WFDC2, MSLN and MMP7--warrant further evaluation in precious serum specimens collected months to years prior to clinical diagnosis to assess their utility in early detection. We also reported differences in the performance of these candidate blood markers across histological types of epithelial ovarian cancer.By systematically analyzing the performance of candidate blood markers of ovarian cancer in distinguishing women with clinically apparent ovarian cancer from women without ovarian cancer, we identified a set of serum markers with adequate performance to warrant testing for their ability to identify ovarian cancer months to years prior to clinical diagnosis. We argued for the importance of sensitivity at high specificity and of magnitude of difference in marker levels between cases and controls as performance metrics and demonstrated the importance of stratifying analyses by histological type of ovarian cancer. Also, we discussed the limitations of studies (like this one) that use samples obtained from symptomatic women to assess potential utility in detection of disease months to years prior to clinical detection

Medical student case presentation performance and perception when using mobile learning technology in the emergency department

Hand-held mobile learning technology provides opportunities for clinically relevant self-instructional modules to augment traditional bedside teaching. Using this technology as a teaching tool has not been well studied. We sought to evaluate medical students&amp;rsquo; case presentation performance and perception when viewing short, just-in-time mobile learning videos using the iPod touch prior to patient encounters.Twenty-two fourth-year medical students were randomized to receive or not to receive instruction by video, using the iPod Touch, prior to patient encounters. After seeing a patient, they presented the case to their faculty, who completed a standard data collection sheet. Students were surveyed on their perceived confidence and effectiveness after using these videos.Twenty-two students completed a total of 67 patient encounters. There was a statistically significant improvement in presentations when the videos were viewed for the first time (p&#x200A;=&#x200A;0.032). There was no difference when the presentations were summed for the entire rotation (p&#x200A;=&#x200A;0.671). The reliable (alpha&#x200A;=&#x200A;0.97) survey indicated that the videos were a useful teaching tool and gave students more confidence in their presentations.Medical student patient presentations were improved with the use of mobile instructional videos following first time use, suggesting mobile learning videos may be useful in medical student education. If direct bedside teaching is unavailable, just-in-time iPod touch videos can be an alternative instructional strategy to improve first-time patient presentations by medical students