Stephan M. Winkler

MS Amanda, a Universal Identification Algorithm Optimized for High Accuracy Tandem Mass Spectra

Today’s highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring algorithm particularly suitable for high mass accuracy. Our algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at http://ms.imp.ac.at/?goto=msamanda, is provided free of charge both as standalone version for integration into custom workflows and as a plugin for the Proteome Discoverer platform.

Architecture and Design of the HeuristicLab Optimization Environment

Many optimization problems cannot be solved by classical mathematical optimization techniques due to their complexity and the size of the solution space. In order to achieve solutions of high quality though, heuristic optimization algorithms are frequently used. These algorithms do not claim to find global optimal solutions, but offer a reasonable tradeoff between runtime and solution quality and are therefore especially suitable for practical applications. In the last decades the success of heuristic optimization techniques in many different problem domains encouraged the development of a broad variety of optimization paradigms which often use natural processes as a source of inspiration (as for example evolutionary algorithms, simulated annealing, or ant colony optimization). For the development and application of heuristic optimization algorithms in science and industry, mature, flexible and usable software systems are required. These systems have to support scientists in the development of new algorithms and should also enable users to apply different optimization methods on specific problems easily. The architecture and design of such heuristic optimization software systems impose many challenges on developers due to the diversity of algorithms and problems as well as the heterogeneous requirements of the different user groups. In this chapter the authors describe the architecture and design of their optimization environment HeuristicLab which aims to provide a comprehensive system for algorithm development, testing, analysis and generally the application of heuristic optimization methods on complex problems.

Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis

There are several data based methods in the field of artificial intelligence which are nowadays frequently used for analyzing classification problems in the context of medical applications. As we show in this paper, the application of enhanced evolutionary computation techniques to classification problems has the potential to evolve classifiers of even higher quality than those trained by standard machine learning methods. On the basis of five medical benchmark classification problems taken from the UCI repository as well as the Melanoma data set (prepared by members of the Department of Dermatology of the Medical University Vienna) we document that the enhanced genetic programming approach presented here is able to produce comparable or even better results than linear modeling methods, artificial neural networks, kNN classification, support vector machines and also various genetic programming approaches.

Effects of constant optimization by nonlinear least squares minimization in symbolic regression

In this publication a constant optimization approach for symbolic regression is introduced to separate the task of finding the correct model structure from the necessity to evolve the correct numerical constants. A gradient-based nonlinear least squares optimization algorithm, the Levenberg-Marquardt (LM) algorithm, is used for adjusting constant values in symbolic expression trees during their evolution. The LM algorithm depends on gradient information consisting of partial derivations of the trees, which are obtained by automatic differentiation. The presented constant optimization approach is tested on several benchmark problems and compared to a standard genetic programming algorithm to show its effectiveness. Although the constant optimization involves an overhead regarding the execution time, the achieved accuracy increases significantly as well as the ability of genetic programming to learn from provided data. As an example, the Pagie-1 problem could be solved in 37 out of 50 test runs, whereas without constant optimization it was solved in only 10 runs. Furthermore, different configurations of the constant optimization approach (number of iterations, probability of applying constant optimization) are evaluated and their impact is detailed in the results section.

Advanced Genetic Programming Based Machine Learning

A Genetic Programming based approach for solving classification problems is presented in this paper. Classification is understood as the act of placing an object into a set of categories, based on the object’s properties; classification algorithms are designed to learn a function which maps a vector of object features into one of several classes. This is done by analyzing a set of input-output examples (“training samples”) of the function. Here we present a method based on the theory of Genetic Algorithms and Genetic Programming that interprets classification problems as optimization problems: Each presented instance of the classification problem is interpreted as an instance of an optimization problem, and a solution is found by a heuristic optimization algorithm. The major new aspects presented in this paper are advanced algorithmic concepts as well as suitable genetic operators for this problem class (mainly the creation of new hypotheses by merging already existing ones and their detailed evaluation). The experimental part of the paper documents the results produced using new hybrid variants of Genetic Algorithms as well as investigated parameter settings. Graphical analysis is done using a novel multiclass classifier analysis concept based on the theory of Receiver Operating Characteristic curves.

NOx Virtual Sensor Based on Structure Identification and Global Optimization

On-line measurement of engine NOx emissions is the object of a substantial effort, as it would strongly improve the control of Cl engines. Many efforts have been directed towards hardware solutions, in particular to physical sensors, which have already reached a certain degree of maturity. In this paper, we are concerned with an alternative approach, a virtual sensor, which is essentially a software code able to estimate the correct value of an unmeasured variable, thus including in some sense an input/output model of the process. Most virtual sensors are either derived by fitting data to a generic structure (like an artificial neural network, ANN) or by physical principles. In both cases, the quality of the sensor tends to be poor outside the measured values. In this paper, we present a new approach: the data are screened for hidden analytical structures, combining structure identification and evolutionary algorithms, and these structures are then used to develop the sensor presented. While the computational time for the sensor design can be significant (e.g. 1 or more hours), the resulting formula is very compact and proves able to predict the behaviour of the system at other operating points. The method has been validated with NOx data from a production engine measured with a Horiba Mexa 7000. The approach is able to yield a good prediction behaviour over a whole cycle. The results are consistent with physical knowledge.

Using FE Calculations and Data-Based System Identification Techniques to Model the Nonlinear Behavior of PMSMs

This paper investigates the modeling of brushless permanent-magnet synchronous machines (PMSMs). The focus is on deriving an automatable process for obtaining dynamic motor models that take nonlinear effects, such as saturation, into account. The modeling is based on finite element (FE) simulations for different current vectors in the

On text preprocessing for opinion mining outside of laboratory environments

dq plane over a full electrical period. The parameters obtained are the stator flux in terms of the direct and quadrature components and the air-gap torque, both modeled as functions of the rotor angle and the current vector. The data are preprocessed according to theoretical results on potential harmonics in the targets as functions of the rotor angle. A variety of modeling strategies were explored: linear regression, support vector machines, symbolic regression using genetic programming, random forests, and artificial neural networks. The motor models were optimized for each training technique, and their accuracy was then compared based on the initially available FE data and further FE simulations for additional current vectors. Artificial neural networks and symbolic regression using genetic programming achieved the highest accuracy, particularly with additional test data.

Benefits of plugin-based heuristic optimization software systems

Opinion mining deals with scientific methods in order to find, extract and systematically analyze subjective information. When performing opinion mining to analyze content on the Web, challenges arise that usually do not occur in laboratory environments where prepared and preprocessed texts are used. This paper discusses preprocessing approaches that help coping with the emerging problems of sentiment analysis in real world situations. After outlining the identified shortcomings and presenting a general process model for opinion mining, promising solutions for language identification, content extraction and dealing with Internet slang are discussed.

Quantification and Kinetic Analysis of Grb2-EGFR Interaction on Micro-Patterned Surfaces for the Characterization of EGFR-Modulating Substances

Plugin-based software systems are the next step of evolution in application development. By supporting fine grained modularity not only on the source code but also on the post-compilation level, plugin frameworks help to handle complexity, simplify application configuration and deployment, and enable users or third parties to easily enhance existing applications with self-developed modules without having access to the whole source code. In spite of these benefits, plugin-based software systems are seldom found in the area of heuristic optimization. Some reasons for this drawback are discussed, several benefits of a plugin-based heuristic optimization software system are highlighted and some ideas are shown, how a heuristic optimization meta-model as the basis of a thorough plugin infrastructure for heuristic optimization could be defined.