Národní úložiště šedé literatury Nalezeno 110 záznamů.  1 - 10dalšíkonec  přejít na záznam: Hledání trvalo 0.00 vteřin. 
Modelování a analýza logistických procesů pomocí procesních a datových analytických metod
Rudnitckaia, Julia ; Wang, Hao (oponent) ; Zendulka, Jaroslav (oponent) ; Hruška, Tomáš (vedoucí práce)
V této práci navrhujeme přístup k modelování skrytých a neznámých procesů a podprocesů na příkladu logistického námořního přístavu. Základní procesní model umožňuje využívat pokročilejší algoritmy, protože odchylky a hlavní cesty jsou viditelnější a lépe kontrolovatelné. Získaný model je základem pro stěžejní výzkum této práce a bude obohacen o klíčové ukazatele výkonnosti a jejich predikce použitím pokročilých technik procesního dolování, statistiky a strojového učení. Hlavní rozdíl v přístupu je v tom, že jako cílovou proměnnou nebereme žádnou konkrétní hodnotu, ale objekt - variantu procesu nebo typ procesu se sadou parametrů. Analýza úzkých míst na jedné straně a predikční analýza na druhé straně jsou vynuceny informacemi, které jsou zlepšené pomoci context-aware informace, zejména těmito dalšími objektivními atributy procesu. Kromě toho podpora deskriptivního ("Jak je") aktuálního procesního modelu s určitou notace a integrace s relevantními úzkými a prediktivními metodami kompromitují výhody tohoto přístupu. Práce se primárně zaměřuje na návrh algoritmů a metod pro podporu analýzy logistických dat. Lze jej však odpovídajícím způsobem upravit a aplikovat na jiné oblasti, díky čemuž je přístup flexibilní a universální. Výsledkem práce je framework pro modelování nestrukturovaných procesů a metoda predikce klíčových parametrů procesů. Tato analýza procesů s jejich atributy by mohla být v budoucnu využita pro systémy rozhodování a procesní mapy.
Mining of soluble enzymes from genomic databases
Hon, Jiří ; Brejová, Bronislava (oponent) ; Šafránek, David (oponent) ; Zendulka, Jaroslav (vedoucí práce)
Enzymes are proteins accelerating chemical reactions, which makes them attractive targets for both pharmaceutical and industrial applications. The enzyme function is mediated by several essential amino acids which form the optimal chemical environment to catalyse the reaction. In this work, two integrated bioinformatics tools for mining and rational selection of novel soluble enzymes, EnzymeMiner and SoluProt, are presented. EnzymeMiner uses one or more enzyme sequences as input along with a description of essential residues to search the protein database. The description of essential amino acids is used to increase the probability of similar enzymatic function. EnzymeMiner output is a set of annotated database hits. EnzymeMiner integrates taxonomic, environmental, and protein domain annotations to facilitate selection of promising targets for experiments. The main prioritization criterion is solubility predicted by the second tool being presented, SoluProt.  SoluProt is a machine-learning method for the prediction of soluble protein expression in Escherichia coli . The input is a protein sequence and the output is the probability of such protein to be soluble. SoluProt exploits a gradient boosting machine to decide on the output prediction class. The tool was trained on TargetTrack database. When evaluated against a balanced independent test set derived from the NESG database, SoluProt accuracy was 58.5% and its AUC 0.62, slightly exceeding those of a suite of alternative solubility prediction tools. Both EnzymeMiner and SoluProt are frequently used by the protein engineering community to find novel soluble biocatalysts for chemical reactions. These have a great potential to decrease energetic consumption and environmental burden of many industrial chemical processes.
Detection of repetitive sequences in genomes
Puterová, Janka ; Jedlička, Pavel (oponent) ; Kléma, Jiří (oponent) ; Zendulka, Jaroslav (vedoucí práce)
Repetitive sequences can make up a significant part of the genome, in some cases more than 80%, but scientists have often overlooked them. Today we know that repeats have various functions in the genomes and are divided into two main groups: interspersed and tandem repeats. This work aimed to develop bioinformatics tools to detect repetitive sequences, either directly from sequencing data generated by sequencers or assembled genomes. In the introductory part, the work provides an insight into the issue and an overview of the repeat types occurring in genomes. Furthermore, the work deals with existing approaches and tools with an aim to detect repeats directly from the assembled sequences. The main contribution to this area was developing the digIS tool, which aims to detect insertion sequences that represent the most abundant interspersed repeats in prokaryotes. digIS is based on the principle of profile hidden Markov models constructed for the catalytic domains of transposases, representing the most conserved part of the insertion sequences and retaining a secondary structure within the family. Subsequently, the work provides an overview of sequencing technologies and discusses existing methods for detecting repeats directly from sequencing data without the need for prior genome assembly. A novel approach for a detailed analysis of tandem repeats is presented. This approach extends the primary analysis of RepeatExplorer, which detects and characterizes repeats directly from sequencing data. The work further discusses the applications of repeat detection in biological research, especially from the point of view of comparative repeatome studies and the evolution of sex chromosomes. Finally, the work summarizes the research results in the form of four articles published in international journals, the full text of which is available in the appendices, and provides a general summary of the work together with possibilities for future research.
Computational Design of Stable Proteins
Musil, Miloš ; Lexa, Matej (oponent) ; Vinař, Tomáš (oponent) ; Zendulka, Jaroslav (vedoucí práce)
Stable proteins are utilized in a vast number of medical and biotechnological applications. However, the native proteins have mostly evolved to function under mild conditions inside the living cells. As a result, there is a great interest in increasing protein stability to enhance their utility in the harsh industrial conditions. In recent years, the field of protein engineering has matured to the point that enables tailoring of native proteins for specific practical applications. However, the identification of stable mutations is still burdened by costly and laborious experimental work. Computational methods offer attractive alternatives that allow a rapid search of the pool of potentially stabilizing mutations to prioritize them for further experimental validation. A plethora of the computational strategies was developed: i) force-field-based energy calculations, ii) evolution-based techniques, iii) machine learning, or iv) the combination of several approaches. Those strategies are usually limited in their predictions to less impactful single-point mutations, while some more sophisticated methods for prediction of multiple-point mutations require more complex inputs from the side of the user. The main aim of this Thesis is to provide users with a fully automated workflow that would allow for the prediction of the highly stable multiple-point mutants without the requirement of the extensive knowledge of the bioinformatics tools and the protein of interest. FireProt is a fully automated workflow for the design of the highly stable multiple-point mutants. It is a hybrid method that combines both energy- and evolution-based approaches in its calculation core, utilizing sequence information as a filter for robust force-field calculations. FireProt workflow not only detects a pool of potentially stabilizing mutations but also tries to combine them together while reducing the risk of antagonistic effects. FireProt-ASR is a fully automated workflow for ancestral sequence reconstruction, allowing users to utilize this protein engineering strategy without the need for the laborious manual work and the knowledge of the system of interest. It resolves all the steps required during the process of ancestral sequence reconstruction, including the collection of the biologically relevant homologs, construction of the rooted tree, and the reconstruction of the ancestral sequences and ancestral gaps.HotSpotWizard is a workflow for the automated design of mutations and smart libraries for the engineering of protein function and stability. It allows for a wider analysis of the protein of interest by utilizing four different protein engineering strategies: i) identification of the highly mutable residues located in the catalytic pockets and tunnels, ii) identification of the flexible regions, iii) calculation of the sequence consensus, and iv) identification of the correlated residues.FireProt-DB is a database of the known experimental data quantifying a protein stability. The main aim of this database is to standardize protein stability data, provide users with well-manageable storage, and allow them to construct protein stability datasets to use them as training sets for various machine learning applications.
Protein Classification Techniques
Dekrét, Lukáš ; Zendulka, Jaroslav (oponent) ; Burgetová, Ivana (vedoucí práce)
Main goal of classifying proteins into families is to understand structural, functional and evolutionary relationships between individual proteins, which are not easily deducible from available data. Since the structure and function of proteins are closely related, determination of function is mainly based on structural properties, that can be obtained relatively easily with current resources. Protein classification is also used in development of special medicines, in the diagnosis of clinical diseases or in personalized healthcare, which means a lot of investment in it. I created a new hierarchical tool for protein classification that achieves better results than some existing solutions. The implementation of the tool was preceded by acquaintance with the properties of proteins, examination of existing classification approaches, creation of an extensive data set, realizing experiments and selection of the final classifiers of the hierarchical tool.
Complex Validator for Web Pages
Horvát, Jozef ; Zendulka, Jaroslav (oponent) ; Volf, Tomáš (vedoucí práce)
This thesis deals with the creation of a complex validator for web pages using HTML, CSS and JavaScript. The application is implemented using the Angular framework. In addition to validation, the application provides suggestions for error correction.
Nástroj pro ladění definice platebního formátu
Kuba, Richard ; Rychlý, Marek (oponent) ; Zendulka, Jaroslav (vedoucí práce)
Hlavním cílem této práce je vyvinout a demonstrovat nástroj pro ladění platebních formátů, který by uživatelům transakce (programu) DMEEX usnadňoval odhalování chyb v definičních stromech. Demonstrace nástroje pro ladění je implementována na platformě SAP S/4HANA. V první části práce jsou popisovány platformy SAP R/3 a SAP S/4HANA, s důrazem na vystižení rozdílů mezi nimi. Dále je pak rozebrána problematika platebních formátů a jejich podpora v rámci systémů SAP. V rámci návrhu je popsán sběr a práce s požadavky uživatelů na tento nástroj. Implementovaný produkt umožňuje uživatelům vizualizovat průběh zpracování definičního stromu transakce DMEEX, díky čemuž jeho uživatelům umožňuje jednodušší odhalování chyb v definici stromu nebo v jeho vstupních datech.
Vícejazyčná podpora v systému OKbase
Podsedník, Lukáš ; Kunc, Michael (oponent) ; Zendulka, Jaroslav (vedoucí práce)
OKbase je kompletní softwarový produkt pro firmy zahrnující docházkový, mzdový a personální modul. Je implementován a provozován v českém jazyce. Cílem tohoto projektu je rozšířit OKbase tak, aby bylo snadné postupně doplňovat podporu dalších jazyků.
Generic Decentralized Self-Adaptive Context-Aware Architecture Model
Kazzaz, M. Mohanned ; Zimmerová, Barbora (oponent) ; Vranić,, Valentino (oponent) ; Zendulka, Jaroslav (vedoucí práce)
The evolution in information system continuously raises demands for more efficient, effective and adaptive cooperation between system's components to cope with changes in the system and to guarantee its best performance. Two main approaches have been introduced to achieve these requirements. First, the self-adaptation approach which enables information system to adapt to the changes in context information of the system and its surrounding environment based on an adaptation strategy. Second, context-awareness approach which enables to monitor the context information and recognize those changes that can trigger the adaptation process. In this work we introduce a generic context-aware self-adaptive architecture model to support software system with adaptation functionalities that guarantee system's availability, operation conditions and performance. Moreover, we provide two real-life case studies as a proof-of-concept of the applicability and re-usability of our proposed adaptation approach.
Algoritmus pro cílené doporučování produktů
Bodeček, Miroslav ; Bartík, Vladimír (oponent) ; Zendulka, Jaroslav (vedoucí práce)
Tato diplomová práce se zabývá prozkoumáním problematiky doporučování produktů v internetovém obchodování, zhodnocením dostupných technik, detailním návrhem systému doporučování produktů pro existující internetový obchod a implementací tohoto systému včetně otestování. V technické zprávě je nejprve prezentován úvod do problematiky, představen současný stav v internetovém obchodování a specifikovány požadavky na implementaci nadstavby nad internetovým obchodem. Dále zpráva obsahuje úvod do dolování dat. Následuje detailní návrh systému a zpráva o provedeném testování. Závěr obsahuje zhodnocení dosažených výsledků a diskuzi o možném dalším vývoji.

Národní úložiště šedé literatury : Nalezeno 110 záznamů.   1 - 10dalšíkonec  přejít na záznam:
Chcete být upozorněni, pokud se objeví nové záznamy odpovídající tomuto dotazu?
Přihlásit se k odběru RSS.