Markus Borschbach

A Computational Approach for the Identification of Small GTPases Based on Preprocessed Amino Acid Sequences

The prediction of essential biological features based on a given protein sequence is a challenging task in computational biology. To limit the amount of in vitro verification, the prediction of essential biological activities gives the opportunity to detect so far unknown sequences with similar properties. Besides the application within the identification of proteins being involved in tumorigenesis, other functional classes of proteins can be predicted. The prediction accuracy depends on the selected machine learning approach and even more on the composition of the descriptor set used. A computational approach based on feedforward neural networks was applied for the prediction of small GTPases. Consequently, this was realized by taking secondary structure and hydrophobicity information as a preprocessing architecture and thus, as descriptors for the neural networks. We developed a neural network cluster, which consists of a filter network and four subfamily networks. The filter network was trained to identify small GTPases and the subfamily networks were trained to assign a small GTPase to one of the subfamilies. The accuracy of the prediction, whether a given sequence represents a small GTPase is very high (98.25%). The classifications of the subfamily networks yield comparable accuracy. The high prediction accuracy of the neural network cluster developed, gives the opportunity to suggest the use of hydrophobicity and secondary structure prediction in combination with a neural network cluster, as a promising method for the prediction of essential biological activities.

An evolutionary approach for solving the rubik's cube incorporating exact methods

Solutions calculated by Evolutionary Algorithms have come to surpass exact methods for solving various problems. The Rubik’s Cube multiobjective optimization problem is one such area. In this work we present an evolutionary approach to solve the Rubik’s Cube with a low number of moves by building upon the classic Thistlethwaite’s approach. We provide a group theoretic analysis of the subproblem complexity induced by Thistlethwaite’s group transitions and design an Evolutionary Algorithm from the ground up including detailed derivation of our custom fitness functions. The implementation resulting from these observations is thoroughly tested for integrity and random scrambles, revealing performance that is competitive with exact methods without the need for pre-calculated lookup-tables.

A model of inhomogeneity for ad hoc networks

The major prerequisites for successful wireless ad hoc networking are an almost homogeneous distribution of a nontrivial number of nodes and the determination of an almost ideal selective connectivity of the nodes in the network. To give a basic characterization of network connectivity, an ad hoc network model based on planar graphs is introduced. According to this underlying mathematical network description, the features of homogeneous connectivity for ad hoc networks are defined. Due to a specific physical layer ratio of wireless capacity utilization, a condition of isolation gives the opportunity to maintain isolated areas in any given ad hoc network distribution. To support identified isolated regions is a main goal of a hybrid transfer network.

Assessing the Utility of Mobile Applications with Support for or as Replacement of Hearing Aids

Abstract Hearing aids are becoming more and more important in an aging society. One of the main aspects is speech intelligibility. This requires algorithms that separate speech signals from other sources of sound and filter the latter ones away from the source. Such algorithms have a high computational cost and are usually not employed on hearing aids themselves. Most importantly, algorithms implemented on embedded systems like on hearing aids are not universally portable to any other device, whereas such flexibility exists to a much higher degree on smartphone operating systems. The growing prevalence and high processing power of smartphones (including their operating systems) on the market gives rise to the idea of running the algorithms on a smartphone app instead (after recording the input signal with multiple microphones attached to the phone), and having the results sent to a hearing aid for playback. A state-of-the-art assessment of mobile applications that provide related functionality is necessary to determine the necessary fields of improvement and to determine technical, already feasible solutions and applied features in the market. This requires a detailed development of criteria that effectively and formally measure the utility of such applications. This paper develops such criteria and demonstrates a way to apply them in practice.

Impact of different recombination methods in a mutation-specific MOEA for a biochemical application

Peptides play a key role in the development of drug candidates and diagnostic interventions, respectively. The design of peptides is cost-intensive and difficult in general for several well-known reasons. Multi-objective evolutionary algorithms (MOEAs) introduce adequate in silico methods for finding optimal peptides sequences which optimize several molecular properties. A mutation-specific fast non-dominated sorting GA (termed MSNSGA-II) was especially designed for this purpose. In this work, an empirical study is presented about the performance of MSNSGA-II which is extended by optionally three different recombination operators. The main idea is to gain an insight into the significance of recombination for the performance of MSNSGA-II in general - and to improve the performance with these intuitive recombination methods for biochemical optimization. The benchmark test for this study is a three-dimensional optimization problem, using fitness functions provided by the BioJava library.

Introduction of a mutation specific fast non-dominated sorting GA evolved for biochemical optimizations

In many physiochemical and biological phenomena, molecules have to comply with multiple optimized biophysical feature constraints. Mathematical modeling of these biochemical problems consequently results in multi-objective optimization. This study presents a special fast non-dominated sorting genetic algorithm (GA) incorporating different types of mutation (referred to as MSNSGA-II) for resolving multiple diverse requirements for molecule bioactivity with an early convergence in a comparable low number of generations. Hence, MSNSGA-II is based on a character codification and its performance is benchmarked via a specific three-dimensional optimization problem. Three objective functions are provided by the BioJava library: Needleman Wunsch algorithm, hydrophilicity and molecular weight. The performance of our proposed algorithm is tested using several mutation operators: A deterministic dynamic, a self-adaptive, a dynamic adaptive and two further mutation schemes with mutation rates based on the Gaussian distribution. Furthermore, we expose the comparison of MSNSGA-II with the classic NSGA-II in performance.

Augmenting Reputation-Based Trust Metrics with Rumor-Like Dissemination of Reputation Information

Trust is an important and frequently studied concept in personal interactions and business ventures. As such, it has been examined by multitude of scientists in diverse disciplines of study. Over the past years, proposals have been made to model trust relations computationally, either to assist users or for modeling purposes in multi-agent systems. These models rely implicitly on the social networks established by participating entities (be they autonomous agents or internet users). At the same time, research in complex networks has revealed mechanisms of information diffusion, such as the spread of rumors in a population. By adapting rumor-spreading processes to reputation dissemination in multi-agent systems, this paper shows the benefit of augmenting an existing trust model with pro-actively, socially filtered trust information.

A Benchmark on the Interaction of Basic Variation Operators in Multi-objective Peptide Design Evaluated by a Three Dimensional Diversity Metric and a Minimized Hypervolume

Peptides play a key role in the development of drug candidates and diagnostic interventions. The design of peptides is cost-intensive and difficult in general for several well-known reasons. Multi-objective evolutionary algorithms (MOEAs) introduce adequate in silico methods for finding optimal peptide sequences which optimize several molecular properties. A mutation-specific fast non-dominated sorting GA (termed MSNSGA-II) is especially designed for this purpose.

Reputation-Based Trust Diffusion in Complex Socio-Economic Networks

Trust and reputation form the foundation of most human interactions, they are ubiquitous in everyday life. Over the past years, attempts have been made to model trust relations computationally, either to assist users or for modeling purposes in multi-agent systems. As a fundamentally social phenomenon, trust forms, operates on and changes social networks, an aspect not investigated in detail so far. In this chapter, we aim to investigate how the nature of social networks, such as their quality of being highly clustered, impacts the spread and thus the availability of data to agents. Furthermore, we will propose an extension to state-of-the art trust frameworks that leverages the capabilities of information spreading in complex networks by decoupling the provisioning process of reputation information from non-neighboring recommenders.

NUI for an Artificial Simulation of an Interactive Sound Source Enhancement to Restore Spatial Listening Experience

Abstract A self-determined, autonomous and socially integrated life is nearly impossible for hearing-impaired persons of all ages. Hearing damage affects 16% of the European and 11.5% of the US population, but only every fifth is wearing a hearing aid as they are not able to restore the biological listening experience and allow no interactive influence on it. Thus, a natural user interface is presented as an artificial simulation for an interactive enhancement of the user-preferred sound source. Therefore, established blind source separation algorithms are ported on a smartphone with Android operating system and their performance is evaluated regarding efficiency and robustness.