Attributes Calculation for Prediction of Mutation Effect on Protein Function

Tato práce se zabývá problematikou z oblasti bioinformatiky, algoritmů a datových typů a strojového učení. Základem práce jsou již existující aplikace Caver a Deleterious, na jejichž vývoji se podíleli studenti Fakulty informatiky Masarykovy univerzity a Fakulty informačních technologíí Vysokého učení technického v Brně. Aplikace Deleterious slouží k získávání a výpočtu atributů proteinů důležitých k predikci vlivu mutace proteinu na jeho výslednou funkci a Caver je program pro hledání tunelů v prostorovém modelu proteinu. Výsledkem má být rozšíření těchto aplikací o výpočet nových atributů, které mohou přispět ke zlepšení přesnosti predikce. Přidané atributy souvisí s hledáním a měřením kapes proteinu.This thesis deals with issues of bioinformatics, machine learning, algorithms and data structures. The thesis is based on existing applications, Caver and Deleterious, developed by students from the Faculty of Informatics, Masaryk University and the Faculty of Information Technology, Brno University of Technology. The Deleterious framework calculates protein attributes that are important for the prediction of the effect of protein mutations on its function. Caver is a tool that finds tunnels in the 3-dimensional model of a protein. The goal of the thesis is to extend these applications by adding more attributes to the prediction process that could lead to improved prediction. The added attributes are related to detection and measurement of protein pockets.

ToyArchitecture: Unsupervised Learning of Interpretable Models of the World

Research in Artificial Intelligence (AI) has focused mostly on two extremes: either on small improvements in narrow AI domains, or on universal theoretical frameworks which are usually uncomputable, incompatible with theories of biological intelligence, or lack practical implementations. The goal of this work is to combine the main advantages of the two: to follow a big picture view, while providing a particular theory and its implementation. In contrast with purely theoretical approaches, the resulting architecture should be usable in realistic settings, but also form the core of a framework containing all the basic mechanisms, into which it should be easier to integrate additional required functionality. In this paper, we present a novel, purposely simple, and interpretable hierarchical architecture which combines multiple different mechanisms into one system: unsupervised learning of a model of the world, learning the influence of one's own actions on the world, model-based reinforcement learning, hierarchical planning and plan execution, and symbolic/sub-symbolic integration in general. The learned model is stored in the form of hierarchical representations with the following properties: 1) they are increasingly more abstract, but can retain details when needed, and 2) they are easy to manipulate in their local and symbolic-like form, thus also allowing one to observe the learning process at each level of abstraction. On all levels of the system, the representation of the data can be interpreted in both a symbolic and a sub-symbolic manner. This enables the architecture to learn efficiently using sub-symbolic methods and to employ symbolic inference.Comment: Revision: changed the pdftitl

Attributes Calculation for Prediction of Mutation Effect on Protein Function

This thesis deals with issues of bioinformatics, machine learning, algorithms and data structures. The thesis is based on existing applications, Caver and Deleterious, developed by students from the Faculty of Informatics, Masaryk University and the Faculty of Information Technology, Brno University of Technology. The Deleterious framework calculates protein attributes that are important for the prediction of the effect of protein mutations on its function. Caver is a tool that finds tunnels in the 3-dimensional model of a protein. The goal of the thesis is to extend these applications by adding more attributes to the prediction process that could lead to improved prediction. The added attributes are related to detection and measurement of protein pockets

Process Control with Dynamic Resource Scheduling

Práce se zabývá mezioborovou problematikou na pomezí informačních technologií a~optimalizace procesů. Jsou zde využity a rozšířeny dříve navržené postupy pro modelování projektů a~zdrojů pomocí objektově orientovaných Petriho sítí. Dále se rozebírají možnosti použití genetických algoritmů pro optimalizaci rozvrhů zdrojů, které určují jejich přiřazení k~jednotlivým aktivitám v~dynamických systémech. Je popsána třída rozvrhovacích problémů s omezenými zdroji a způsob, jakým lze tyto projekty implementovat. Také je ukázáno, jak lze vytvořit složitější model výroby inspirovaný reálnými výrobními procesy. Dále je navržen řídící agent, který sleduje běžící výrobní systém a~umožňuje jeho dynamickou optimalizaci. Celý systém je implementován v prostředí Squeak Smalltalk za využití nástroje PNtalk, který je experimentální implementací objektově orientovaných sítí.This project pursues issues on the border of information technologies and process optimization. Previously published concepts of~modeling projects and shared resources with object-oriented Petri nets are presented and further expanded. The possibilites of~the use of~genetic algorithms for dynamic realtime optimization of the resource schedules are explored. The resource constrained project sheduling problem is presented and it is shown, how instances of the problem can be implemented. A more complex model that is inspired by real production systems is then created. Next, a control agent, which monitors a running production system and allows for it's dynamic optimization is designed. The whole system is implemented in the Squeak Smalltalk environment with the use of the tool PNtalk, which is an experimental implementation of the object oriented Petri nets paradigm.