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VQ-VAE is a successful generative model which can perform lossy compression. It combines deep learning with vector quantization to achieve a discrete compressed representation of the data. We explore using different vector quantization techniques with VQ-VAE, mainly neural gas and fuzzy c-means. Moreover, VQ-VAE consists of a non-differentiable discrete mapping which we will explore and propose changes to the original VQ-VAE loss to fit the alternative vector quantization techniques.
Protein structures are essential elements in every biological system evolved on earth, where they function as stabilizing elements, signaltransducers or replication machin eries. They are consisting of linear-bonded amino acids, which determine the three-dimensional structure of the protein, whereas the structure in turn determines the function. The native and biological active structure ofa protein can be understood as the folding state of a polypeptide chain at the global minimum of free energy.
By means of protein energy profiling, which is an approach derived from statistical physics it is possible to assign a so called energy profile to a protein structure. Such an energy profile describes the local energetic interaction features of every amino acid within the structure and introduces an energetic point of view, instead of a structural or sequential onto proteins.
This work aims to give a perspective to the question of how we may gain pattern information out of energy profiles. The concrete subjects are energy-mapped Pfam family alignments and investigations on finding motifs or patterns indiscretizised energy profile segments.
In Machine Learning, Learning Vector Quantization(LVQ) is well known as supervised learning method. LVQ has been studied to generate optimal reference vectors because of its simple and fast learning algorithm [12]. In many tasks of classification, different variants of LVQ are considered while training a model. In this thesis, the two variants of LVQ, Generalized Matrix Learning Vector Quantization(GMLVQ) and Generalized Tangent Learning Vector Quantization(GTLVQ) have been discussed. And later, transfer learning technique for different variants of LVQ has been implemented, visualized and we have compared the results using different datasets.
Community acquired pneumonia (CAP) is a very common, yet infectious and sometimes lethal disease. Therefor, this disease is connected to high costs of diagnosis and treatment. To actually reduce the costs for health care in this matter, diagnosis and treatment must get cheaper to conduct with no loss in predictive accuracy. One effective way in doing so would be the identification of easy detectable and highly specific transcriptomic markers, which would reduce the amount of work required for laboratory tests by possibly enhanced diagnosis capability.
Transcriptomic whole blood data, derived from the PROGRESS study was combined with several documented features like age, smoking status or the SOFA score. The analysis pipeline included processing by self organizing maps for dimensionality and noise reduction, as well as diffusion pseudotime (DPT). Pseudotime enabled modelling a disease run of CAP, where each sample represented a state/time in the modelled run. Both methods combined resulted in a proposed disease run of CAP, described by 1476 marker genes. The additional conduction of a geneset analysis also provided information about the immune related functions of these marker genes.
Influenza A viruses are responsible for the outbreak of epidemics as well as pandemics worldwide. The surface protein neuraminidase of this virus is responsible, among other things, for the release of virions from the cell and is thus of interest in pharmacological research. The aim of this work is to gain knowledge about evolutionary changes in sequences of influenza A neuraminidase through different methods. First, EVcouplings is used with the goal of identifying evolutionary couplings within the protein sequences, but this analysis was unsuccessful. This is probably due to the great sequence length of neuraminidase. Second, the natural vector method will be used for sequence embedding purposes, in hopes to visualize sequential progression of the virus protein over time. Last, interpretable machine learning methods will be applied to examine if the data is classifiable by the different years and to gain information if the extracted information conform to the results from the EVcouplings analysis. Additionally to using the class label year, other labels such as groups or subtypes are used in classification with varying results. For balanced classes the machine learning models performed adequately, but this was not the case for imbalanced data. Groups and subtypes can be classified with a high accuracy, which was not the case for the years, continents or hosts. To identify the minimal number of features necessary for linear separation of neuraminidase group 1 subtypes, a logistic regression was performed at last, resulting in the identification of 15 combinations of nine amino acid frequencies. Since the sequence embedding as well as the machine learning methods did not show neuraminidase evolution over time, further research is necessary, for example with focus on one subtype with balanced data.
Proteins are involved in almost every aspect of life, mediating a wide range of cellular tasks. The protein sequence dictates the spatial arrangement of the residues and thus ultimately the function of a rotein. Huge effort is put into cumbersome structure eludication experiments which obtain models describing the observed spatial conformation of a protein, enabling users to predict their function, to understand their mode of action or to design tailored drugs to cure disease caused by misfolded or misregulated proteins.
However, the result of structure determination experiments are merely models of reality, made under simplifying assumptions - sometimes containing major undetected errors. On the other hand, such experiments are resource demanding and they cannot supply the actual demand.
Thus, scientists are predicting the structure of proteins in silico, resulting in models that are even
more prone to error.
In consequence, the structure biologists search after a practicable definition of structure quality and over the last two decades several model quality assessment programs emerged, measuring the local and global quality of peculiar structures. Seven representatives were studied, regarding the paradigms they follow and the features they use to describe the quality of residues. Their predications were compared, showing that there is almost no common ground among the tools.
Is there a way to combine their statements anyway?
Finally, the accumulated knowledge was used to design a novel evaluation tool, addressing problems previously spotted. Thereby, high quality of its predication as well as superior usability was
key. The strategy was compared to existing approaches and evaluated on suitable datasets.
The theoretical foundations of enterprise management using information technology were reviewed; analysis of the effectiveness of the use of information systems in the enterprise; ways of improving the enterprise management mechanism using information systems (on example of Mars Wrigley Confectionery Belarus) have been developed.
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This master thesis covers the topics of Studying customers’ behavior on the example of skin care brand Nivea. There are presented theoretical basis for the following research about marketing, customers’ behavior and conducting marketing research properly. Then, there is the analysis of German market. Since Nivea is the brand of Beiersdorf company, there is a description of Beiersdorf’s activity and operation work. The main idea of the paper work is to analyze customers’ behavior of Nivea. Therefore, the work contains huge research about the brand along with its’ micro- and macroenvironment. There also were conducted an in-depth interview and a survey to understand customers’
current needs. With all the results the author of the work proposed some ideas for Nivea brand.
This study shows the potential for the make-or-buy theory in several scenarios – production, assembling and development. The evaluation of these possibilities is conducted, based on Bosch’s core competencies. A decision model is developed to support the decision making process. Based on these results, the serial production at RBAC in China is planned and suggestions for setting up the assembly line are given