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Application for the Data Processing in the Area of Genome Engineering
Brychta, Jan ; Burgetová, Ivana (referee) ; Očenášek, Pavel (advisor)
This masters thesis has a few objectives. One of them is to acquaint with the problems of genome engineering, especially with fragmentation of DNA, the macromolecule DNA, the methods for purification and separation of the nucleic acids, the enzymes used for modification of these acids, amplification and get to know with cluster and gradient analysis as well. The next aim is to peruse the existed application and compare it to the layout of the proposed application, that is the third aim. The last one from the objectives is the implementation and the report how was the application tested by the real data. The results will be discussed as well as the possibilities of the further extension.
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Automatic Chord Recognition Using Deep Neural Networks
Nodžák, Petr ; Bidlo, Michal (referee) ; Vašíček, Zdeněk (advisor)
This work deals with automatic chord recognition using neural networks. The problem was separated into two subproblems. The first subproblem aims to experimental finding of most suitable solution for a acoustic model and the second one aims to experimental finding of most suitable solution for a language model. The problem was solved by iterative method. First a suboptimal solution of the first subproblem was found and then the second one. A total of 19 acoustic and 12 language models were made. Ten training datasets was created for acoustic models and three for language models. In total, over 200 models were trained. The best results were achieved on acoustic models represented by convolutional networks together with language models represented by recurent networks with LSTM modules.
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Analysis of training dataset influence on the efficiency of segmentation
Benešovská, Veronika ; Vičar, Tomáš (referee) ; Jakubíček, Roman (advisor)
Microbial structures are present in every living organism, so it is important to classify them for subsequent research of their origin and function. Bruker, s.r.o is developing the MBT Pathfinder for this purpose, which automates the transfer of colonies to MALDI plates, where the subsequent analysis of the sample takes place. Transferred colonies can be selected manually or using an algorithm that ensures automatic colony segmentation. This algorithm must be learned on a training set, which has huge influence on its accuracy. This work deals with measuring the influence of a dataset on the accuracy of this learning algorithm.
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Analysis of training dataset influence on the efficiency of segmentation
Benešovská, Veronika ; Vičar, Tomáš (referee) ; Jakubíček, Roman (advisor)
Microbial structures are present in every living organism, so it is important to classify them for subsequent research of their origin and function. Bruker, s.r.o is developing the MBT Pathfinder for this purpose, which automates the transfer of colonies to MALDI plates, where the subsequent analysis of the sample takes place. Transferred colonies can be selected manually or using an algorithm that ensures automatic colony segmentation. This algorithm must be learned on a training set, which has huge influence on its accuracy. This work deals with measuring the influence of a dataset on the accuracy of this learning algorithm.
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|
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Automatic Chord Recognition Using Deep Neural Networks
Nodžák, Petr ; Bidlo, Michal (referee) ; Vašíček, Zdeněk (advisor)
This work deals with automatic chord recognition using neural networks. The problem was separated into two subproblems. The first subproblem aims to experimental finding of most suitable solution for a acoustic model and the second one aims to experimental finding of most suitable solution for a language model. The problem was solved by iterative method. First a suboptimal solution of the first subproblem was found and then the second one. A total of 19 acoustic and 12 language models were made. Ten training datasets was created for acoustic models and three for language models. In total, over 200 models were trained. The best results were achieved on acoustic models represented by convolutional networks together with language models represented by recurent networks with LSTM modules.
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|
|
Application for the Data Processing in the Area of Genome Engineering
Brychta, Jan ; Burgetová, Ivana (referee) ; Očenášek, Pavel (advisor)
This masters thesis has a few objectives. One of them is to acquaint with the problems of genome engineering, especially with fragmentation of DNA, the macromolecule DNA, the methods for purification and separation of the nucleic acids, the enzymes used for modification of these acids, amplification and get to know with cluster and gradient analysis as well. The next aim is to peruse the existed application and compare it to the layout of the proposed application, that is the third aim. The last one from the objectives is the implementation and the report how was the application tested by the real data. The results will be discussed as well as the possibilities of the further extension.
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