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Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

Abstract Pressure–composition–temperature (P–c–T) relations of the La-incorporated and fluorinated AB 2 alloy, Zr 0.9 Ti 0.1 V 0.2 Mn 0.6 Co 0.1 Ni 1.1 , were studied under isothermal dynamic conditions of 313 K and 343 K. The La-incorporation and fluorination effectively improved both the significant deviations existing in the static and dynamic P–c–T relations of conventional metal hydrides.

Abstract. Background: ROC analysis is widely accepted to assess and compare diagnostic validity of laboratory tests. Within the last few years, many new ROC pro

Many metrics are currently used to evaluate the performance of ranking methods in virtual screening (VS), for instance, the area under the receiver operating characteristic curve (ROC), the area under the accumulation curve (AUAC), the average rank of actives, the enrichment factor (EF), and the robust initial enhancement (RIE) proposed by Sheridan et al. In this work, we show that the ROC, the AUAC, and the average rank metrics have the same inappropriate behaviors that make them poor metrics for comparing VS methods whose purpose is to rank actives early in an ordered list (the “early recognition problem”). In doing so, we derive mathematical formulas that relate those metrics together. Moreover, we show that the EF metric is not sensitive to ranking performance before and after the cutoff. Instead, we formally generalize the ROC metric to the early recognition problem which leads us to propose a novel metric called the Boltzmann-enhanced discrimination of receiver operating characteristic that turns out to contain the discrimination power of the RIE metric but incorporates the statistical significance from ROC and its well-behaved boundaries. Finally, two major sources of errors, namely, the statistical error and the “saturation effects”, are examined. This leads to practical recommendations for the number of actives, the number of inactives, and the “early recognition” importance parameter that one should use when comparing ranking methods. Although this work is applied specifically to VS, it is general and can be used to analyze any method that needs to segregate actives toward the front of a rank-ordered list.

Abstract Pressure–composition–temperature (P–c–T) relations of the La-incorporated and fluorinated AB 2 alloy, Zr 0.9 Ti 0.1 V 0.2 Mn 0.6 Co 0.1 Ni 1.1 , were studied under isothermal dynamic conditions of 313 K and 343 K. The La-incorporation and fluorination effectively improved both the significant deviations existing in the static and dynamic P–c–T relations of conventional metal hydrides.

For widely applied in silico screening techniques success depends on the rational selection of an appropriate method. We herein present a fast, versatile, and robust method to construct demanding evaluation kits for objective in silico screening (DEKOIS). This automated process enables creating tailor-made decoy sets for any given sets of bioactives. It facilitates a target-dependent validation of docking algorithms and scoring functions helping to save time and resources. We have developed metrics for assessing and improving decoy set quality and employ them to investigate how decoy embedding affects docking. We demonstrate that screening performance is target-dependent and can be impaired by latent actives in the decoy set (LADS) or enhanced by poor decoy embedding. The presented method allows extending and complementing the collection of publicly available high quality decoy sets toward new target space. All present and future DEKOIS data sets will be made accessible at www.dekois.com.

The application of molecular benchmarking sets helps to assess the actual performance of virtual screening (VS) workflows. To improve the efficiency of structure-based VS approaches, the selection and optimization of various parameters can be guided by benchmarking. With the DEKOIS 2.0 library, we aim to further extend and complement the collection of publicly available decoy sets. Based on BindingDB bioactivity data, we provide 81 new and structurally diverse benchmark sets for a wide variety of different target classes. To ensure a meaningful selection of ligands, we address several issues that can be found in bioactivity data. We have improved our previously introduced DEKOIS methodology with enhanced physicochemical matching, now including the consideration of molecular charges, as well as a more sophisticated elimination of latent actives in the decoy set (LADS). We evaluate the docking performance of Glide, GOLD, and AutoDock Vina with our data sets and highlight existing challenges for VS tools. All DEKOIS 2.0 benchmark sets will be made accessible at http://www.dekois.com.

Objective: To investigate the diagnostic value of dynamic serum procalcitonin (PCT) level in recurrent infection and cut-off value for PCT. Method: From which such cases as voluntary discharge, death and generally accepted conditions affecting PCT levels were ruled out, leaving 300 cases taken into our research, including 200 cases of recurrent infection and 100 cases of effective treatment. The PCT levels for the recurrent cases were compared with those receiving effective treatment simultaneously. Furthermore, differences between PCT rebound points for the recurrent and those from 60 control cases were compared. The diagnostic performance of PCT, WBC count, and IL-6 for recurrent infection cases was evaluated using ROC curve. Results: The PCT level differed for cases with recurrent infections and those receiving effective treatment. The former demonstrated a rebound in PCT level. The PCT rebound point for recurrent cases was higher than that of the normal controls. The area under the ROC of PCT for the diagnosis of recurrent infection was 83.5%. The cut-off level corresponding to the optimal sensitivity (89%), specificity (83%), positive predictive value (84%), and negative predictive value (88.3%) was 2.29 ng·ml-1. However, the WBC count and IL-6 level had little diagnostic value for recurrent infection, and the area under the ROC curve was 55.1% and 61.0%, respectively. Conclusion: Dynamic monitoring of serum PCT levels had an important diagnostic and discriminatory value for recurrent infection induced by bacteria.

A key metric to assess molecular docking remains ligand enrichment against challenging decoys. Whereas the directory of useful decoys (DUD) has been widely used, clear areas for optimization have emerged. Here we describe an improved benchmarking set that includes more diverse targets such as GPCRs and ion channels, totaling 102 proteins with 22886 clustered ligands drawn from ChEMBL, each with 50 property-matched decoys drawn from ZINC. To ensure chemotype diversity, we cluster each target’s ligands by their Bemis–Murcko atomic frameworks. We add net charge to the matched physicochemical properties and include only the most dissimilar decoys, by topology, from the ligands. An online automated tool (http://decoys.docking.org) generates these improved matched decoys for user-supplied ligands. We test this data set by docking all 102 targets, using the results to improve the balance between ligand desolvation and electrostatics in DOCK 3.6. The complete DUD-E benchmarking set is freely available at http://dude.docking.org.

Objective: To investigate the diagnostic value of dynamic serum procalcitonin (PCT) level in recurrent infection and cut-off value for PCT. Method: From which such cases as voluntary discharge, death and generally accepted conditions affecting PCT levels were ruled out, leaving 300 cases taken into our research, including 200 cases of recurrent infection and 100 cases of effective treatment. The PCT levels for the recurrent cases were compared with those receiving effective treatment simultaneously. Furthermore, differences between PCT rebound points for the recurrent and those from 60 control cases were compared. The diagnostic performance of PCT, WBC count, and IL-6 for recurrent infection cases was evaluated using ROC curve. Results: The PCT level differed for cases with recurrent infections and those receiving effective treatment. The former demonstrated a rebound in PCT level. The PCT rebound point for recurrent cases was higher than that of the normal controls. The area under the ROC of PCT for the diagnosis of recurrent infection was 83.5%. The cut-off level corresponding to the optimal sensitivity (89%), specificity (83%), positive predictive value (84%), and negative predictive value (88.3%) was 2.29 ng·ml-1. However, the WBC count and IL-6 level had little diagnostic value for recurrent infection, and the area under the ROC curve was 55.1% and 61.0%, respectively. Conclusion: Dynamic monitoring of serum PCT levels had an important diagnostic and discriminatory value for recurrent infection induced by bacteria.

Ligand enrichment among top-ranking hits is a key metric of molecular docking. To avoid bias, decoys should resemble ligands physically, so that enrichment is not simply a separation of gross features, yet be chemically distinct from them, so that they are unlikely to be binders. We have assembled a directory of useful decoys (DUD), with 2950 ligands for 40 different targets. Every ligand has 36 decoy molecules that are physically similar but topologically distinct, leading to a database of 98 266 compounds. For most targets, enrichment was at least half a log better with uncorrected databases such as the MDDR than with DUD, evidence of bias in the former. These calculations also allowed 40 × 40 cross-docking, where the enrichments of each ligand set could be compared for all 40 targets, enabling a specificity metric for the docking screens. DUD is freely available online as a benchmarking set for docking at http://blaster.docking.org/dud/.

The production of granules by wet granulation in a fluidized bed was assessed after the construction of a quality index based on a file of attributes (relevant factors). These attributes are combined by a methodology relying on Correspondence Analysis, as a discriminant procedure, using two extreme …

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MachiningCloud fast-tracks your shop’s entire workflow. With easy access to manufacturers’ product data and application knowledge, you can power-up your digital shop in ways never before possible.

Abstract Receiver operating characteristics (ROC) curve with the calculation of area under curve (AUC) is a useful tool to evaluate the performance of biomedical and chemoinformatics data. For example, in virtual drug screening ROC curves are very often used to visualize the efficiency of the used application to separate active ligands from inactive molecules. Unfortunately, most of the available tools for ROC analysis are implemented into commercially available software packages, or are plugins in statistical software, which are not always the easiest to use. Here, we present Rocker, a simple ROC curve visualization tool that can be used for the generation of publication quality images. Rocker also includes an automatic calculation of the AUC for the ROC curve and Boltzmann-enhanced discrimination of ROC (BEDROC). Furthermore, in virtual screening campaigns it is often important to understand the early enrichment of active ligand identification, for this Rocker offers automated calculation routine. To enable further development of Rocker, it is freely available (MIT-GPL license) for use and modifications from our web-site ( http://www.jyu.fi/rocker ). Graphical Abstract

Receiver operating characteristic (ROC) analysis provides the most comprehensive description of diagnostic accuracy available to date, because it estimates and reports all of the combinations of sensitivity and specificity that a diagnostic test is able to provide. After sketching the 6 levels at wh …

Among the main electric components in power systems, load models have great effect on power system time- domain dynamic simulations. However, owing to the characteristics such as time-variation, randomness and so on, it is difficult to construct load model. In addition, in order to analyze the uncertainties, Monte-Carlo method is often used to estimate the probability density function of the desired output, which requires many sample data obtained from repeated simulations, therefore the uncertainty studies on power system stability are few. This paper studies the effect of the uncertainties of dynamic load proportion on power system dynamic stability based on Probabilistic Collocation Method (PCM). It could be concluded that using the proposed method the desired output is directly described as a polynomial in the uncertain parameter of the system. Consequently, high calculation accuracy could be reached by low-dimensionality polynomial functions and fewer time-domain simulations. Cases study verifies the validity of the proposed method.

Sensitivity and specificity are key measures of the performance of a given test in detecting a given disorder. For tests yielding numerical scores, se…

Among the main electric components in power systems, load models have great effect on power system time- domain dynamic simulations. However, owing to the characteristics such as time-variation, randomness and so on, it is difficult to construct load model. In addition, in order to analyze the uncertainties, Monte-Carlo method is often used to estimate the probability density function of the desired output, which requires many sample data obtained from repeated simulations, therefore the uncertainty studies on power system stability are few. This paper studies the effect of the uncertainties of dynamic load proportion on power system dynamic stability based on Probabilistic Collocation Method (PCM). It could be concluded that using the proposed method the desired output is directly described as a polynomial in the uncertain parameter of the system. Consequently, high calculation accuracy could be reached by low-dimensionality polynomial functions and fewer time-domain simulations. Cases study verifies the validity of the proposed method.