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Comparison of similarities of mass spectra and structures of small molecules
Malíčková, Viktorie ; Galgonek, Jakub (advisor) ; Škrhák, Vít (referee)
Methods for measuring the similarity of mass spectra and the structures of small molecules are crucial for advancements in medicinal chemistry, pharmacology, and metabolomics. One commonly used method for comparing the mass spectra of molecules is cosine similarity. This measures the similarity between two non-zero vectors by calculating the cosine of the angle between them. Comparing the mass spectra of molecules enables searching in molecular databases, clustering of spectra, and exploration of spectral libraries. Structural similarity is measured based on various molecular fingerprints, such as Daylight, RDKit, Atom-Pair, Topological Torsion, Extended-Connectivity fingerprints, and others. These fingerprints are compared using similarity coefficients. The methods for comparing structures and mass spectra of molecules mentioned can be applied using bioinformatic libraries such as RDKit and CDK for generating and analyzing structural fingerprints, and the MatchMS library for comparing mass spectra. The work provides a theoretical overview of molecular descriptors, including various types of molecular fingerprints and techniques for measuring structural similarity, as well as the principles of mass spectrometry and approaches to comparing mass spectra. The practical part of the work focuses on...
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Data Mining Methods for Text Analysis
Kozák, Ondřej ; Marcoň, Petr (referee) ; Dohnal, Přemysl (advisor)
This bachelor thesis explores the current methodology and possibilities of text mining and the subsequent application of some methods. The thesis described methods for preprocessing, methods for converting text to vector space and methods for text analysis and discusses their possible applications. The different preprocessing methods were applied to the text and then the conversion to vector space was demonstrated using simple methods such as BOW, Bag of n-grams, TF-IDF or with machine learning methods which are FastText and GloVe. LSA, LDA, TextRank and cosine similarity methods were applied to the extracted vectors to extract information from the text.
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Web Application of Recommender System
Koníček, Igor ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This master's thesis describes creation of recommender system that is used in real server cbdb.cz. A~fully operational recommender system was developed using collaborative and content-based filtering techniques. Thanks to many user feedback, we were able to evaluate their opinion. Many recommended books were tagged as desirable. This thesis is extending current functionality of cbdb.cz with recommender system. This system uses its extensive database of ratings, users and books.
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Quality Analysis of Electronic Dictionaries Transformation
Stehlíková, Petra ; Škoda, Petr (referee) ; Kouřil, Jan (advisor)
The bachelor's thesis deals with electronic dictionaries, their formats and quality analysis of their conversions. The thesis describes Lexical Markup Framework format in detail. It also discusses the capabilities of advanced algorithms such as LSA for conversion quality analysis and the tools that can be used for the analysis. Based on this theoretical knowledge the scripts in Python language were created to analyze dictionaries in Lexical Markup Framework format.
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Algorithms for anomaly detection in data from clinical trials and health registries
Bondarenko, Maxim ; Blaha, Milan (referee) ; Schwarz, Daniel (advisor)
This master's thesis deals with the problems of anomalies detection in data from clinical trials and medical registries. The purpose of this work is to perform literary research about quality of data in clinical trials and to design a personal algorithm for detection of anomalous records based on machine learning methods in real clinical data from current or completed clinical trials or medical registries. In the practical part is described the implemented algorithm of detection, consists of several parts: import of data from information system, preprocessing and transformation of imported data records with variables of different data types into numerical vectors, using well known statistical methods for detection outliers and evaluation of the quality and accuracy of the algorithm. The result of creating the algorithm is vector of parameters containing anomalies, which has to make the work of data manager easier. This algorithm is designed for extension the palette of information system functions (CLADE-IS) on automatic monitoring the quality of data by detecting anomalous records.
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Automatic Testing of JavaScript Restrictor Project
Bednář, Martin ; Pluskal, Jan (referee) ; Polčák, Libor (advisor)
The aim of the thesis was to design, implement and evaluate the results of automatic tests for the JavaScript Restrictor project, which is being developed as a web browser extension. The tests are divided into three levels - unit, integration, and system. The Unit Tests verify the behavior of individual features, the Integration Tests verify the correct wrapping of browser API endpoints, and the System Tests check that the extension does not suppress the desired functionality of web pages. The System Tests are implemented for parallel execution in a distributed environment which has succeeded in achieving an almost directly proportional reduction in time with respect to the number of the tested nodes. The benefit of this work is detection of previously unknown errors in the JavaScript Restrictor extension and provision of the necessary information that allowed to fix some of the detected bugs.
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Algorithms for anomaly detection in data from clinical trials and health registries
Bondarenko, Maxim ; Blaha, Milan (referee) ; Schwarz, Daniel (advisor)
This master's thesis deals with the problems of anomalies detection in data from clinical trials and medical registries. The purpose of this work is to perform literary research about quality of data in clinical trials and to design a personal algorithm for detection of anomalous records based on machine learning methods in real clinical data from current or completed clinical trials or medical registries. In the practical part is described the implemented algorithm of detection, consists of several parts: import of data from information system, preprocessing and transformation of imported data records with variables of different data types into numerical vectors, using well known statistical methods for detection outliers and evaluation of the quality and accuracy of the algorithm. The result of creating the algorithm is vector of parameters containing anomalies, which has to make the work of data manager easier. This algorithm is designed for extension the palette of information system functions (CLADE-IS) on automatic monitoring the quality of data by detecting anomalous records.
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Web Application of Recommender System
Hlaváček, Pavel ; Bartík, Vladimír (referee) ; Zendulka, Jaroslav (advisor)
This thesis deals with problems of recommender systems and their usage in web applications. There are three main data mining techniques summarized and individual approaches for recommendation. Main part of this thesis is a suggestion and an implementation of web applications for recommending dishes from restaurants. Algorithm for food recommending is designed and implemented in this paper. The algorithm deals with the problem of frequently changing items. The algorithm utilizes hybrid filtering technique which is based on content and knowledge. This filtering technique uses cosine vector similarity for computation.
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Data Mining Methods for Text Analysis
Kozák, Ondřej ; Marcoň, Petr (referee) ; Dohnal, Přemysl (advisor)
This bachelor thesis explores the current methodology and possibilities of text mining and the subsequent application of some methods. The thesis described methods for preprocessing, methods for converting text to vector space and methods for text analysis and discusses their possible applications. The different preprocessing methods were applied to the text and then the conversion to vector space was demonstrated using simple methods such as BOW, Bag of n-grams, TF-IDF or with machine learning methods which are FastText and GloVe. LSA, LDA, TextRank and cosine similarity methods were applied to the extracted vectors to extract information from the text.
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Automatic Testing of JavaScript Restrictor Project
Bednář, Martin ; Pluskal, Jan (referee) ; Polčák, Libor (advisor)
The aim of the thesis was to design, implement and evaluate the results of automatic tests for the JavaScript Restrictor project, which is being developed as a web browser extension. The tests are divided into three levels - unit, integration, and system. The Unit Tests verify the behavior of individual features, the Integration Tests verify the correct wrapping of browser API endpoints, and the System Tests check that the extension does not suppress the desired functionality of web pages. The System Tests are implemented for parallel execution in a distributed environment which has succeeded in achieving an almost directly proportional reduction in time with respect to the number of the tested nodes. The benefit of this work is detection of previously unknown errors in the JavaScript Restrictor extension and provision of the necessary information that allowed to fix some of the detected bugs.
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