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Potential calculation of mutual information from a time series
Hubr, Ivo ; Smékal, Zdeněk (referee) ; Mekyska, Jiří (advisor)
Mutual information is one of the factors used in traffic analysis and preparation phase space. Begin of this work deal with information theory, focusing on the calculation of mutual information. To calculate this parameter has been available for many algorithms which are analyzing in this final work. Two of the algorithms (Fraser-Swinney and calculation of mutual information using adaptive XY subdivision) are applied to the input data Rössler’ attractor, as shown in the output tables and graphs. The third consideration method is the computational Dinh-Tuan-Pham algorithm. The main goal of this work is a comparison of efficiency, speed and accuracy of the calculation of these algorithms.
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Detection of Correlated Mutations
Ižák, Tomáš ; Bendl, Jaroslav (referee) ; Martínek, Tomáš (advisor)
Tato práce zkoumá existující možnosti a metody detekce korelovaných mutací v proteinech. Práce začíná teoretickým úvodem do zkoumané problematiky. Využití informací o korelovaných mutacích je především při predikci terciální struktury proteinu či hledání oblastí s významnou funkcí. Dále následuje přehled v současnosti používaných metod detekce a jejich výhody a nevýhody. V této práci jsou zkoumány zejména metody založené na statistice (například Pearsonově korelačním koeficientu nebo Pearsonově chi^2 testu), informační teorii (Mutual information - MI) a pravděpodobnosti (ELSC nebo Spidermonkey). Dále jsou popsány nejdůležitější nástroje s informací o tom, které metody používají a jakým způsobem. Také je diskutována možnost návrhu optimálního algoritmu. Jako optimální z hlediska úspěšnosti detekce je doporučeno využít více zmíněných metod. Také je doporučeno při detekci využít fyzikálně-chemických vlastností aminokyselin. V praktické části byla vyvinuta metoda využívající fyzikálně-chemických vlastností aminokyselin a fylogenetických stromů. Výsledky detekce byly porovnány s nástroji CAPS, CRASP a CMAT.
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Application of statistical analysis of speech in patients with Parkinson's disease
Bijota, Jan ; Mžourek, Zdeněk (referee) ; Galáž, Zoltán (advisor)
This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.
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Gene regulatory network inference based on mutual information in non-model organisms
Pirkl, Petr ; Sedlář, Karel (referee) ; Musilová, Jana (advisor)
The thesis is focused on summary of laboratory methods for determining gene expression, data preprocessing procedures and possible tools used to infere gene regulatory networks. Furthermore, the thesis handles with the pre-processing of data. It means create count table and normalize it. It was use data from the non-model organism Clostridium beijerinckii NRRL B-598. The main parts of the thesis are designed an algorithm for the creation of a gene regulatory network using mutual information and its implementation in the R language. This include testing the algorithm on data from the non-model organism and the gold standard.
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Transcription motif finding in non-model organisms
Helešicová, Klára ; Jurečková, Kateřina (referee) ; Musilová, Jana (advisor)
This bachelor thesis deals with the search for DNA motifs in non-model organisms. The first part explains the process of transcription, the concept of mutual information and an algorithm using mutual information. The second part describes the distribution of motif searching methods and examples of algorithms. The third part contains an overview of transcription motif databases. The practical part contains a description of the creation of a dataset for a non-model organism, a description of the proposed algorithm and its testing on the dataset. Then, the results of the proposed algorithm were compared with the results of FIRE and MEME algorithms.
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Advanced registration of image sequences from video-ophthalmoscope
Dufková, Barbora ; Chmelík, Jiří (referee) ; Kolář, Radim (advisor)
This master's thesis deals with the issue of registration of ophthalmic video sequences. It describes basic geometric transformations that can be used for registration. The basic methods of image registration are also presented, from which the most suitable variant for this application is selected. This is then implemented using a script created in the MATLAB environment. The proposed method is further evaluated objectively using the brightness profile method, using mutual information and correlation, and using retinal vessel skeleton. The effect of polynomial transformation on registration and possible optimizations of the algorithm are discussed.
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Transcription motif finding in non-model organisms
Helešicová, Klára ; Jurečková, Kateřina (referee) ; Musilová, Jana (advisor)
This bachelor thesis deals with the search for DNA motifs in non-model organisms. The first part explains the process of transcription, the concept of mutual information and an algorithm using mutual information. The second part describes the distribution of motif searching methods and examples of algorithms. The third part contains an overview of transcription motif databases. The practical part contains a description of the creation of a dataset for a non-model organism, a description of the proposed algorithm and its testing on the dataset. Then, the results of the proposed algorithm were compared with the results of FIRE and MEME algorithms.
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Gene regulatory network inference based on mutual information in non-model organisms
Pirkl, Petr ; Sedlář, Karel (referee) ; Musilová, Jana (advisor)
The thesis is focused on summary of laboratory methods for determining gene expression, data preprocessing procedures and possible tools used to infere gene regulatory networks. Furthermore, the thesis handles with the pre-processing of data. It means create count table and normalize it. It was use data from the non-model organism Clostridium beijerinckii NRRL B-598. The main parts of the thesis are designed an algorithm for the creation of a gene regulatory network using mutual information and its implementation in the R language. This include testing the algorithm on data from the non-model organism and the gold standard.
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Bioinformatic methods of detection of protein coevolution
Pařízková, Hana ; Schneider, Bohdan (advisor) ; Hampl, Vladimír (referee)
The term coevolution describes the situation when two or more species or biomole- cules reciprocally affect each others' evolution. On the protein level, it is thought to be the main mechanism ensuring correct folding, interactions and function of a protein, and it can be observed both on the level of interacting protein families and individual amino acid residues. Coevolution studies have been proved to be a powerful tool for prediction of protein structure, function, interaction partners, etc. In this thesis, different algorithms used for detection of protein coevolution are described, as well as their applications and limitations. Keywords: coevolution, protein family, protein structure prediction, interac- tion partners, correlated mutations, mirrortree, mutual information, direct cou- pling analysis
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Application of statistical analysis of speech in patients with Parkinson's disease
Bijota, Jan ; Mžourek, Zdeněk (referee) ; Galáž, Zoltán (advisor)
This thesis deals with speech analysis of people who suffer from Parkinson’s disease. Purpose of this thesis is to obtain statistical sample of speech parameters which helps to determine if examined person is suffering from Parkinson’s disease. Statistical sample is based on hypokinetic dysarthria detection. For speech signal pre-processing DC-offset removal and pre-emphasis are used. The next step is to divide signal into frames. Phonation parameters, MFCC and PLP coefficients are used for characterization of framed speech signal. After parametrization the speech signal can be analyzed by statistical methods. For statistical analysis in this thesis Spearman’s and Pearson’s correlation coefficients, mutual information, Mann-Whitney U test and Student’s t-test are used. The thesis results are the groups of speech parameters for individual long czech vowels which are the best indicator of the difference between healthy person and patient suffering from Parkinson’s disease. These result can be helpful in medical diagnosis of a patient.
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