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Methods to detect selection in DNA sequences
Procházka, Ondřej ; Maděránková, Denisa (referee) ; Škutková, Helena (advisor)
The topic of semestral thesis is methods to detect selection in DNA sequences. In the begining of the thesis we will describe molecular evolution. It will be written what made the evolution and how the evolution is shown. Moreover there are gen mutations and mechanisms of diffuse and fixation. It will be defined what pozitive, negative and neutral selection is. The thesis is focused on evolution distance of synonymous and nonsynonymous substitution. There will be described three methods – Nei-Gojobori, Li-Wu-Luo and Comeron. All these methods will be described with mathematic formulas. There will be statistic test to decide what kind of selection ti is – there will be used z-test. In the practical part, there will be information about developed software what counts selection pressure from sequences from databazes in format GenBank and it shows parts where selection is. The software will be used for two data sets with two different genetic codes. The result will be discussed. We will discuss results of all three methods of selection pressure and influence of input parametrs.
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Evolutionary computing
Popelka, Jan ; Smékal, Zdeněk (referee) ; Karásek, Jan (advisor)
The aim of this Bachelor's Thesis was to get acquainted with the Evolutionary Optimization Techniques, mainly with the Genetic Algorithm and Genetic Programming. It was subsequently described the role of optimization problem TSP solved using Genetic Algorithms and other Chapter solving Symbolic Regression using Genetic Programming. This optimalization problems were created in the programming JAVA and there are solved practical part of the thesis.
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Attributes Calculation for Prediction of Mutation Effect on Protein Function
Šinkora, Jan ; Filák, Jakub (referee) ; Jaša, Petr (advisor)
This thesis deals with issues of bioinformatics, machine learning, algorithms and data structures. The thesis is based on existing applications, Caver and Deleterious, developed by students from the Faculty of Informatics, Masaryk University and the Faculty of Information Technology, Brno University of Technology. The Deleterious framework calculates protein attributes that are important for the prediction of the effect of protein mutations on its function. Caver is a tool that finds tunnels in the 3-dimensional model of a protein. The goal of the thesis is to extend these applications by adding more attributes to the prediction process that could lead to improved prediction. The added attributes are related to detection and measurement of protein pockets.
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Travelling Salesman Problem
Kolář, Adam ; Rozman, Jaroslav (referee) ; Zbořil, František (advisor)
The aim of this bachelor's thesis is to design a testing environment for the traveling salesman problem and compare the effectiveness of different approaches to the solution. The first part discussed the possibility of genetic algorithms, depending on the setting of a crossover, mutations and population size. In the second part, there is the same problem using two types of neural networks. The representative of the self-learning net was chosen Kohonen neural network. Hopfield neural network represents a method of minimizing the energy function with fixed coefficients. At both neural networks, there were described possible advantages and disadvantages. In the end, all the findings were interpreted in a global context.
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Discovery of Wireless Sensor Network Topology Using Genetic Algorithms
Dalecký, Štěpán ; Samek, Jan (referee) ; Zbořil, František (advisor)
The thesis deals with a design of the genetic algorithm that is able to discover the wireless sensor network topology using signal strength among particular sensors. At first, the thesis describes the theory of genetic algorithm and wireless sensor network. Subsequently, on the basis of this theory, the genetic algorithm serving for the wireless sensor network topology discovery has been designed. The thesis also describes important features of the algorithm implementation. In conclusion, the outcomes have been reviewed.
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Functional Annotation of Nucleotide Polymorphism Using Evolution Strategy
Šalanda, Ondřej ; Martínek, Tomáš (referee) ; Bendl, Jaroslav (advisor)
This thesis brings a new approach to the prediction of the the effect of amino acid substitution. The main goal is to create a new meta-tool, which combines evaluations of eight already implemented prediction tools. The use of weighted consensus over those tools should lead to better accuracy and versatility of prediction. The novelty of developed tool lies in involving evolution strategy with experimentally defined parameters as a way to determine the best weight distribution. At the end, a complex comparison and evaluation of results is given.
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Development of Meta-Server for Prediction of Mutations Effects on Protein Function
Lisák, Peter ; Burgetová, Ivana (referee) ; Jaša, Petr (advisor)
This bachelor thesis deals with analysis of genomic data, more specifically prediction of effects of mutations on protein function using a protein sequence or tertiary structure. The theoretical introduction describes the basics of genetics and bioinformatics and is followed by description of selected prediction tools such as SIFT, MAPP and AUTO-MUTE. A unified interface for work with different tools is proposed in the thesis. The meta-server interface allows running a computation and collecting results from one site. meta-server combines results of implemented tools and provides a consensual prediction, which is expected to be more accurate than the results from individual tools. Finally, testing of meta-server on the real data and comparisons of predictions with the experimentally obtained results are presented.
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Evolutionary Solving of Rubik's Cube
Kollner, Aleš ; Bidlo, Michal (referee) ; Jaroš, Jiří (advisor)
This thesis deals with solving of the Rubik's cube. It describes the Rubik's cube and the famous methods for its composition. The main goal of this work is to propose an evelutionary method that for any configuration of blocks will lead to its composition. The theis describes the problem encoding, the proposed evelutionary algorithm and its proper configuration and deployment. The achieved results are commented on and compared with other known mehtods in conclusions.
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Prediction of the Effect of Nucleotide Substitution Using Machine Learning
Šalanda, Ondřej ; Martínek, Tomáš (referee) ; Bendl, Jaroslav (advisor)
This thesis brings a new approach to the prediction of the effect of nucleotide polymorphism on human genome. The main goal is to create a new meta-classifier, which combines predictions of several already implemented software classifiers. The novelty of developed tool lies in using machine learning methods to find consensus over those tools, that would enhance accuracy and versatility of prediction. Final experiments show, that compared to the best integrated tool, the meta-classifier increases the area under ROC curve by 3,4 in average and normalized accuracy is improved by up to 7\,\%. The new classifying service is available at http://ll06.sci.muni.cz:6232/snpeffect/.
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