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Benchmark of the Computational Tools for the Prediction of the Effect of Mutations on Protein Stability
Berezný, Matej ; Martínek, Tomáš (referee) ; Musil, Miloš (advisor)
Návrh proteínov vyžaduje informáciu o tom ako mutácie ovplyvňujú celkovú stabilitu proteinu. Pre tento prípad existuje mnoho verejne dostupných nástrojov avšak ich kolektívne používanie či porovnávanie je veľmi pracné. Presne pre tento prípad som vyvinul BenchStab; konzolovú aplikáciu/Python knižnicu navrhnutú pre rýchlu a priamočiaru manipuláciu s 18 prediktormi, umožňujúc hromadné získavanie mutačných výsledkov. Zároveň som vytvoril novú unikátnu dátovú sadu, získanú z FireProtDB. Tento dataset som použil na porovnanie 24 rôznych predikčných metód pomocou rôznych metrík.
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Very Low Bit-Rate Speech Coding Based on Neural Networks
Jochman, Stanislav ; Malenovský, Vladimír (referee) ; Černocký, Jan (advisor)
Vrámci tejto práce sme skúmali možnosti zlepšenia kvality zvuku produkovaným pomocou neurónovej siete LPCNet. Analyzovali sme vplyv použitia dátových setov zameraných na cieľový jazyk a ich vplyv na kvalitu výsledného zvuku. Pre meranie kvality kódovania reči sme využili hodnotiaci systém WARP-Q. Cieľom našej práce bolo navrhnúť vylepšenie trénovacieho dátového setu a použitie postfilterov pre zlepšenie kvality zvuku. Naše výsledky ukazujú merateľné zlepšenia s využitím malého slovenského dátového setu. Rovnako sme zaznamenali, že využitie dolnopriepustného filteru a filtra zlepšujúceho formanty zlepšilo kvalitu výsledného zvuku.
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Optimization of Run Configurations of k-Wave Jobs
Sasák, Tomáš ; Jaroš, Marta (referee) ; Jaroš, Jiří (advisor)
This thesis focuses on scheduling, i.e. correct approximation of configurations used to run k-Wave simulations on supercomputers from the IT4Innovations infrastructure. Especially, for clusters Salomon and Anselm. A single work is composed of a set which contains many simulations. Every simulation is executed by some code from the k-Wave toolbox. To calculate the simulation, it is necesarry to select a suitable configuration, which means the amount of supercomputer resources (number of nodes, i.e. cores), and the duration of the rental. Creation of an ideal configuration is complicated and is even harder for an inexperienced user. The approximation is made based on the empiric data, obtained from multiple executions of different sets of simulations on given clusters. This data is stored and used by a set of approximators, which performs the actual approximation by methods of interpolation and regression. The text describes the implementation of the final scheduler. By experimenting, the most efficient methods for this problem has found out to be Akima spline, PCHIP interpolation and cubic spline. The main contribution of this work is creation of a tool which can find suitable configuration for k-Wave simulation without knowing the code or having lots of experience with its usage.
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The Use of Artificial Intelligence for Decision Making
Nezbedová, Katarína ; Pekárek, Jan (referee) ; Dostál, Petr (advisor)
This bachelor thesis deals with the Tamari attractor problem and its application for forming a prediction model. The core of the work is to create a simulation program in the MATLAB development environment and to use it to create and compare several case studies of a predictive model based on different parameters. This model is graphically illustrated and supplemented by economic interpretation.
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Air Quality Analysis in Office and Residential Areas
Tisovčík, Peter ; Korček, Pavol (referee) ; Kořenek, Jan (advisor)
The goal of the thesis was to study the indoor air quality measurement focusing on the concentration of carbon dioxide. Within the theoretical part, data mining including basic classification methods and approaches to dimensionality reduction was introduced. In addition, the principles of the developed system within IoTCloud project and available possibilities for measurement of necessary quantities were studied. In the practical part, the suitable sensors for given rooms were selected and long-term measurement was performed. Measured data was used to create the system for window opening detection and for the design of appropriate way of air change regulation in a room. The aim of regulation was to improve air quality using natural ventilation.
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Bioinformatics Tool for Prediction of Protein Solubility
Hronský, Patrik ; Burgetová, Ivana (referee) ; Martínek, Tomáš (advisor)
This master's thesis addresses the solubility of recombinant proteins and its prediction. It describes the subject of protein synthesis, as well as the process of recombinant protein creation. Recombinant protein synthesis is of great importance for example to pharmacologic industry. This synthesis is not a simple task and it does not always produce viable proteins. Protein solubility is an important factor, determining the viability of the resulting proteins. It is of course favourable for companies, that take part in recombinant protein synthesis, to focus their effort and their resources on proteins, that will be viable in the end. In this regard, bioinformatics is of great help, as it is capable, with the help of machine learning, of predicting the solubility of proteins, for example based on their sequences. This thesis introduces the reader to the basic principles of machine learning and presents several machine learning methods, used in the field of protein solubility prediction. It deals with the definition of a dataset, which is later used to test selected predictors, as well as to train the ensemble predictor, which is the main focus of this thesis. It also focuses on several specific protein solubility predictors and explains the basic principles upon which they are built, as well as the results of their testing. In the end, it presents the ensemble predictor of protein solubility.
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Machine Learning Optimization of KPI Prediction
Haris, Daniel ; Burget, Radek (referee) ; Bartík, Vladimír (advisor)
This thesis aims to optimize the machine learning algorithms for predicting KPI metrics for an organization. The organization is predicting whether projects meet planned deadlines of the last phase of development process using machine learning. The work focuses on the analysis of prediction models and sets the goal of selecting new candidate models for the prediction system. We have implemented a system that automatically selects the best feature variables for learning. Trained models were evaluated by several performance metrics and the best candidates were chosen for the prediction. Candidate models achieved higher accuracy, which means, that the prediction system provides more reliable responses. We suggested other improvements that could increase the accuracy of the forecast.
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Prediction of Protein Solubility
Marušiak, Martin ; Martínek, Tomáš (referee) ; Hon, Jiří (advisor)
Protein solubility is closely related to the usability of proteins in industrial use and research. The successful prediction of solubility would therefore lead to a significant saving of financial resources. This work presents new solubility predictor Solpex based on machine learning that achieved better performance on independent test set than any comparable solubility prediction tool. The predictor implementation was preceded by a study of the biological nature of solubility, evaluation of existing solubility prediction approaches, datasets building, many experiments with novel features and selection of the best features for the predictor. As the most important step in machine learning is the datasets building, this work mainly benefits from own rigorous processing of the main source of solubility data - the TargetTrack database.
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The Use of Statistical Methods for Data Processing
Matuškovič, Marián ; Štěpánková, Vladěna (referee) ; Novotná, Veronika (advisor)
This master thesis focuses on application of statistical methods in the processing of data. The first part of the thesis describes the theoretical foundations that are the basis for the practical part. Next part of this thesis describes the statistical and financial analysis and also design of an application that automate usage of statistical methods of regression analysis to predict the future economic situation development of the company. This thesis contains theory of time series methods and regression analysis.
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