Parsimonious Kernel Fisher Discrimination

By applying recent results in optimization transfer, a new algorithm for kernel Fisher Discriminant Analysis is provided that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The algorithm is simple, easily programmed and is shown to perform as well as or better than a number of leading machine learning algorithms on a substantial benchmark. It is then applied to a set of extreme small-sample-size problems in virtual screening where it is found to be less accurate than a currently leading approach but is still comparable in a number of cases

A Simple Iterative Algorithm for Parsimonious Binary Kernel Fisher Discrimination

By applying recent results in optimization theory variously known as optimization transfer or majorize/minimize algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the coefficients to provide a parsimonious solution. The problem is converted into a smooth optimization that can be solved iteratively with no greater overhead than iteratively re-weighted least-squares. The result is simple, easily programmed and is shown to perform, in terms of both accuracy and parsimony, as well as or better than a number of leading machine learning algorithms on two well-studied and substantial benchmarks

Activation of heme biosynthesis by a small molecule that is toxic to fermenting Staphylococcus aureus

Staphylococcus aureus is a significant infectious threat to global public health. Acquisition or synthesis of heme is required for S. aureus to capture energy through respiration, but an excess of this critical cofactor is toxic to bacteria. S. aureus employs the heme sensor system (HssRS) to overcome heme toxicity; however, the mechanism of heme sensing is not defined. Here, we describe the identification of a small molecule activator of HssRS that induces endogenous heme biosynthesis by perturbing central metabolism. This molecule is toxic to fermenting S. aureus, including clinically relevant small colony variants. The utility of targeting fermenting bacteria is exemplified by the fact that this compound prevents the emergence of antibiotic resistance, enhances phagocyte killing, and reduces S. aureus pathogenesis. Not only is this small molecule a powerful tool for studying bacterial heme biosynthesis and central metabolism; it also establishes targeting of fermentation as a viable antibacterial strategy

Graphical and numerical analysis of spatial data with a GUI and R

The paper is on the basis of DAS+R1, a package of where the emphasis is on a user friendly menu, a graphical user interface (GUI) enabling the call of different -functions which otherwise would afford a cumbersome typing in of possibly complicated commands for sophisticated methods of statistical data analysis. The package is still under development, nevertheless the actual state of the program system already enables the user to start easily with standard methods and continue with more complicated methods if the clickable commands already exist or by switching to the command line modus. We emphasize the repeatability of the generation of commands in several ways to intensify the speed of obtaining senseful results. A special view is put on the analysis of spatially depending uni- or multivariate data, particularly on problems of geochemical data. We describe in short the important features of data analysis in this field with realized functionalities with its clickable icons in DAS+R. We also give short illustrations on practical examples with geochemical, spatial data

Data Analysis Using R and a Graphical Interface

The computer program system R provides efficient analysis methods for almost any kind of applied problems in statistics and is running on many computer platforms. It is developed on a non-commercial basis and therefore for many practitioners hard to use, nevertheless widely known

Graphical and numerical analysis of spatial data with a GUI and R

The paper is on the basis of DAS+R1, a package of where the emphasis is on a user friendly menu, a graphical user interface (GUI) enabling the call of different -functions which otherwise would afford a cumbersome typing in of possibly complicated commands for sophisticated methods of statistical data analysis. The package is still under development, nevertheless the actual state of the program system already enables the user to start easily with standard methods and continue with more complicated methods if the clickable commands already exist or by switching to the command line modus. We emphasize the repeatability of the generation of commands in several ways to intensify the speed of obtaining senseful results. A special view is put on the analysis of spatially depending uni- or multivariate data, particularly on problems of geochemical data. We describe in short the important features of data analysis in this field with realized functionalities with its clickable icons in DAS+R. We also give short illustrations on practical examples with geochemical, spatial data