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High-performance exploration and querying of selected multi-dimensional spaces in life sciences
Kratochvíl, Miroslav ; Bednárek, David (advisor) ; Glaab, Enrico (referee) ; Svozil, Daniel (referee)
This thesis studies, implements and experiments with specific application-oriented approaches for exploring and querying multi-dimensional datasets. The first part of the thesis scrutinizes indexing of the complex space of chemical compounds, and details a design of high-performance retrieval system for small molecules. The resulting system is then utilized within a wider context of federated search in heterogeneous data and metadata related to the chemical datasets. In the second part, the thesis focuses on fast visualization and exploration of many-dimensional data that originate from single- cell cytometry. Self-organizing maps are used to derive fast methods for analysis of the datasets, and used as a base for a novel data visualization algorithm. Finally, a similar approach is utilized for highly interactive exploration of multimedia datasets. The main contributions of the thesis comprise the advancement in optimization and methods for querying the chemical data implemented in the Sachem database cartridge, the federated, SPARQL-based interface to Sachem that provides the heterogeneous search support, dimensionality reduction algorithm EmbedSOM, design and implementation of the specific EmbedSOM-backed analysis tool for flow and mass cytometry, and design and implementation of the multimedia...
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Methods for Predicting Drug Side Effects in Silico
Cicková, Pavlína ; Lexa,, Matej (referee) ; Berka,, Karel (referee) ; Provazník, Ivo (advisor)
Vývoj a výzkum léčiv je oblastí současné vědy, jejíž nedílnou součástí je i využití výpočetních metod. Z důvodu nákladnosti a časové náročnosti laboratorních přístupů, metody in silico sehrávají svou významnou roli. I přes rychlý vývoj výpočetních technik využívaných při vývoji léků, však není drtivá většina zkoumaných molekul v procesu vývoje úspěšná a do schvalovací fáze nepostoupí. Nejen proto se nejmodernější strategie návrhu potenciálních nových léčiv zaměřují na opětovné zkoumání již schválených léků a berou do úvahy i analýzu podobností. Tato práce popisuje vývoj a aplikaci souboru několika workflow, jež byl vytvořen v rámci analytické platformy KNIME a jež implementuje metody strojového učení za účelem predikce nežádoucích účinků léčiv. Součástí prezentovaných workflow je získání dat, jejich předzpracování, výpočet metrik podobností a provedení explorační analýzy. Následně je využito klasifikačních modelů k predikci specifických nežádoucích účinků léčiv. Tato predikce vychází z principů technik založených na podobnosti. K natrénování modelů rozhodovacích stromů pro predikci potenciální asociace nežádoucích účinků s léčivy byly využity strukturní a jiné podobnosti schválených molekul léčiv. Hlavní přínos práce spočívá především v přenositelnosti použitých metod. Soubor workflow je určen k využití jako vhodný nástroj k řešení výzkumných otázek ohledně podobnosti léčiv a jelikož analytická platforma KNIME poskytuje uživatelsky přívětivé grafické rozhraní, není nutné, aby měli uživatelé pokročilé zkušenosti v oblasti strojového učení nebo programování, aby mohli soubor navržených workflow v rámci této platformy pro své analýzy využít.
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High-performance exploration and querying of selected multi-dimensional spaces in life sciences
Kratochvíl, Miroslav ; Bednárek, David (advisor) ; Glaab, Enrico (referee) ; Svozil, Daniel (referee)
This thesis studies, implements and experiments with specific application-oriented approaches for exploring and querying multi-dimensional datasets. The first part of the thesis scrutinizes indexing of the complex space of chemical compounds, and details a design of high-performance retrieval system for small molecules. The resulting system is then utilized within a wider context of federated search in heterogeneous data and metadata related to the chemical datasets. In the second part, the thesis focuses on fast visualization and exploration of many-dimensional data that originate from single- cell cytometry. Self-organizing maps are used to derive fast methods for analysis of the datasets, and used as a base for a novel data visualization algorithm. Finally, a similar approach is utilized for highly interactive exploration of multimedia datasets. The main contributions of the thesis comprise the advancement in optimization and methods for querying the chemical data implemented in the Sachem database cartridge, the federated, SPARQL-based interface to Sachem that provides the heterogeneous search support, dimensionality reduction algorithm EmbedSOM, design and implementation of the specific EmbedSOM-backed analysis tool for flow and mass cytometry, and design and implementation of the multimedia...
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Accelerating structure search in small-molecule databases
Kratochvíl, Miroslav ; Bednárek, David (advisor) ; Hoksza, David (referee)
Structure search is one of the valuable capabilities of small-molecule databases. Available chemical cartridges typically provide acceptable search performance for processing user queries, but do not scale satisfactorily with dataset size. This thesis presents Sachem, a new open-source chemical car- tridge that implements a novel method of substructure search, which em- ploys newly designed fingerprints stored in inverted indexes. The perfor- mance of the method was assessed on datasets that contain tens of mil- lions of molecules. Comparison of the performance to that of other available cartridges revealed improvements in overall search speed, scaling potential and screen-out efficiency. Additionally, the thesis presents an application of Sachem; a SPARQL service that augments existing semantic services by including results of substructure and similarity searches in small-molecule databases. The result offers new possibilities for simpler querying of the interoperable heterogeneous data sources. 1
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Hierarchical visualization of the chemical space
Velkoborský, Jakub ; Hoksza, David (advisor) ; Škoda, Petr (referee)
The purpose of this thesis was to design and implement a hierarchical approach to visualization of the chemical space. Such visualization is a challenging yet important topic used in diverse fields ranging from material engineering to drug design. Especially in drug design, modern methods of high- throughput screening generate large amounts of data that would benefit from hierarchical analysis. One possible approach to hierarchical classification of molecules is a structure based classification based on molecular scaffolds. The scaffolds are widely used by medicinal chemists to group molecules of similar properties. A few scaffold-based hierarchical visualization methods have been proposed. However, to our best knowledge, there exists no tool that would provide a scaffold-based hierarchical visualization of molecular data sets on the background of known chemical space. In this thesis, such tool was created. First, a scaffold tree hierarchy based on ring topologies was designed. Next, this hierarchy was used to analyze frequency of scaffolds extracted from molecules in PubChem Compound database. Subsequently, the PubChem Compound scaffold frequency data was used as a background for visualization of molecular data sets. The visualization is performed by a client-server application implemented as a part of...
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