Project proposals

• Harnessing the Power of Large Language Models for Conceptual Modeling in Dataspecer

  Parent project: Dataspecer - Date modelling a schema generation

  Conceptual modeling plays a crucial role in data and software engineering, serving as the foundation for designing complex systems and offering a clear representation of the domain. However, creating precise and comprehensive conceptual models remains a challenge for many engineers. In this project, we aim to explore how large language models can be harnessed to develop an intelligent assistant that guides human modelers in creating structured conceptual models from an abundance of existing resources, including unstructured verbose textual sources, emails, and stakeholder interview notes, as well as structured resources, including existing database structures or excel spreadsheets where users collect their proprietary data. The goal is to support and streamline the modeling process, enhancing the overall quality of data and software engineering projects.

  In this project, students will have the opportunity to delve into cutting-edge artificial intelligence and data engineering technologies by working with large language models and applying them to real-world problems. The project's success could open new doors for AI-driven conceptual modeling assistance tools and revolutionize how we design and develop intricate systems. By joining this project, you'll contribute to the future of data and software engineering, honing your skills and making a tangible impact on the field. Don't miss this chance to be part of an innovative research endeavor that could transform the way we approach conceptual modeling in data and software engineering with the help of intelligent assistants.

  Contact: Martin Nečaský

• Revolutionizing Data Querying with AI-Powered Chatbots

  Parent project: Dataspecer - Date modelling a schema generation

  Imagine you're working at your university's data center, where there are countless databases containing tons of valuable information. These databases aren't just used for the school's own systems, but they're also used to generate reports, analyze data, and manage information in general. Basically, the entire school depends on being able to find answers to questions hidden in the data as quickly as possible. But there's a problem - there are only a few employees who really understand both the organization and the databases, and they're the only ones who can translate management's questions into database queries. This creates a bottleneck, where the people who have questions far outnumber the employees who can help them.

  The goal of this project is to create a chatbot that anyone in the organization can use to ask questions. The chatbot will refine the query through conversation and eventually translate it into an evaluatable database query. This means that instead of relying on a few knowledgeable employees, anyone can get the information they need quickly and easily. To make this possible, we'll use Large Language Models (LLMs). But we won't stop there - we'll refine the chatbot's knowledge with a conceptual data model of the organization and technical data artifacts generated from it in Dataspecer.

  By joining this research project, you'll play an important role in transforming how your university's data center operates, making valuable information more accessible to everyone. You'll have the opportunity to work with cutting-edge Large Language Models and contribute to the development of a chatbot that streamlines database querying. This experience will not only enhance your technical skills but also enable you to make a tangible impact on the way data-driven insights are obtained within the organization. Don't miss this chance to be part of an important project that combines advanced technology and practical problem-solving.

  Contact: Martin Nečaský

• Integrace nástrojů pro správu multi-modelových dat

  Parent project: MM-evocat

  Cílem projektu je integrace nástrojů MM-evocat (tvorba schématu multi-modelových dat, realizace změn ve schématu a jejich propagace do dat), MM-infer (odvození schématu z již existujících dat) a MM-quecat (dotazování se nad multi-modelovými daty) a implementace vybrané chybějící funkcionality. Výsledkem bude uživatelsky přívětivý a modulární nástroj, který umožňuje modelování nebo odvození struktury multi-modelových dat, dotazování se nad multi-modelovými daty a realizovat změny ve struktuře dat spolu s propagací těchto změn do dat a úložné strategie.

  Contact: Pavel Koupil

• Web-based plugin for protein-ligand docking results visualization

  Parent project: PrankWeb

  Prediction of protein-ligand binding sites often is one of the first steps in a drug discovery pipeline. Previously, we developed a state-of-the-art tool for protein-ligand binding site prediction called P2Rank that is available either as a stand-alone tool or as a web service, PrankWeb. PrankWeb is currently used by more than 1,000 unique users per month. Recently, we started to extend its capabilities towards enabling different types of analysis over the predicted binding sites. One of such new features is the ability to specify a small molecule and predict how such a molecule would bind to the predicted binding site. This process is called docking. However, the web server currently cannot interactively visualize the docking results. The goal of this project is to develop a web-based plugin for visualization of the docking results using the Mol*, which is currently being used within the web server.
  
  The project does not require any prior knowledge of molecular biology.

  Contact: David Hoksza

• Module for the detection of similar protein-ligand binding sites

  Parent project: PrankWeb

  Prediction of protein-ligand binding sites often is one of the first steps in a drug discovery pipeline. Previously, we developed a state-of-the-art protein-ligand binding site prediction tool called P2Rank, which is available either as a stand-alone tool or as a web service, PrankWeb. PrankWeb is currently used by more than 1,000 unique users per month. Recently, we started to extend its capabilities towards enabling different types of analysis over the predicted binding sites. This project aims to develop a module into PrankWeb, which would seek to detect similar binding sites to the predicted ones in databases of known proteins. That would require coming up with a method for fast detection of similar local protein 3D structures, possibly with the help of FoldSeek, as current structure databases such as AlphaFold Protein Structure Database can contain hundreds of millions of protein structures. Eventually, the developed pipeline will be available as a Docker module to PrankWeb.
  
  The project does not require any prior knowledge of molecular biology.

  Contact: David Hoksza

• Module for the detection and exploration of molecular tunnels starting in predicted protein-ligand binding sites

  Parent project: PrankWeb

  Prediction of protein-ligand binding sites often is one of the first steps in a drug discovery pipeline. Previously, we developed a state-of-the-art protein-ligand binding site prediction tool called P2Rank, which is available either as a stand-alone tool or as a web service, PrankWeb. PrankWeb is currently used by more than 1,000 unique users per month. Recently, we started to extend its capabilities towards enabling different types of analysis over the predicted binding sites. This project aims to develop a module into PrankWeb, which would explore molecular tunnels starting in the predicted binding sites. Quite often, when a molecule binds to a protein, it uses so-called tunnels to travel inside of the protein structure. There exist databases (such as ChannelsDB) of tunnels and also tools (such as MOLE) for the tunnels detection. The module, eventually implemented as a Docker module, would use these external resources to connect the predicted binding sites with tunnels and visualize the results in the PrankWeb interface (an existing visualization library will be used for this, so no knowledge of graphics is needed).
  
  The project does not require any prior knowledge of molecular biology.

  Contact: David Hoksza

• Phosphorilation binding sites prediction with protein language models

  Parent project: PrankWeb

  Protein language models (pLMs) represent a novel technique, harnessing extensive sequence datasets to generate high-dimensional contextual embeddings for every amino acid within a protein sequence. Recently, we have successfully applied pLMs to achieve state-of-the-art performance in predicting protein-ligand binding sites. This project aims to apply pLMs to detect phosphorylation sites. These are specific locations on proteins where a phosphate group attaches, resulting in various downstream processes. This project is in collaboration with the Department of Cell Biology of the Faculty of Science, who will provide the data and biological expertise to enrich the prediction process.

  Contact: David Hoksza

• AlphaFold2 predictions analysis

  Parent project: PrankWeb

  Recently, the DeepMind (Google) team published a deep learning method for protein structure prediction called AlphaFold2 (AF2), which has far surpassed existing methods.

  The goal of this data-driven project is to analyze AF2 predictions. The focus will be on determining how much AF2 actually contributes to the expansion of the protein structure space, with respect to the AF2 "confidence score", which indicates how confident a prediction is at a particular location.

  Contact: David Hoksza

• LP-ETL: Ukládání konfigurace do Solid podů

  Parent project: Nástroj pro transformace webových dat LinkedPipes ETL

  Student se zapojí do vývoje nástroje na získávání, transformaci a ukládání dat s důrazem na propojená (webová) data LinkedPipes ETL, který ve výzkumné skupině dlouhodobě vyvíjíme, a který definuje datové transformace jako procesy složené z propojených, znovuvyužitelných komponent. Nástroj má již řadu produkčních instancí jak v ČR, tak ve světě. Konfigurace transformačních procesů se aktuálně ukládají do souborového systému na serveru, kde je nástroj nasazen. Tématem projektu by bylo umožnit konfigurace ukládat a sdílet v Solid podech - osobních webových datových úložištích využívajících principů propojených dat a decentralizace Webu.

  Contact: Jakub Klímek

• Generování dokumentace a schémat datových formátů na základě ontologického modelu

  Parent project: Dataspecer - Datové modelování a generování schémat

  Data a datová interoperabilita jsou stále důležitější složkou softwarového vývoje. Softwarový systém často stojí na datech, zpracovává je a vyměňuje si je s jinými systémy. K tomu je nutné vyvinout řadu prvků - datová schémata popisující strukturu dat (např. JSON schémata, CSV schémata, XML schémata nebo RDF slovníky), kód obsluhující takto strukturovaná data, transformační kód konvertující data z jedné podoby do druhé (např. mezi dvěma různými JSON formáty) nebo REST API pro výměnu dat mezi různými systémy. V neposlední řadě je pak nutno vše dokumentovat. To je spousta programátorské, testovací a dokumentační práce. Přitom je často zbytečná, protože je rutinní a triviální. V rámci výzkumného projektu se snažíme najít způsoby, jak ji automatizovat. Student se zapojí do výzkumného projektu, v rámci kterého vyvíjíme postupy a software pro generování datové dokumentace a schémat pro různé datové formáty na základě ontologického modelu dané domény (např. turistických cílů).

  Contact: Jakub Klímek

• Generating user-friendly diagrams for data specifications

  Parent project: Dataspecer - Datové modelování a generování schémat

  Currently, when a data specification contains a diagram of what the specification is about, the diagram is created by humans. The diagram is a very important part of the data specification, because it gives people a quick overview of what the specification is about. However, creating a usable layout is very time consuming, and often distracts authors from the actual contents of the specification, when it is being created. The goal of this project is to come up with a convenient way of creating diagrams for data specifications so that the authors can focus on its content, and not have to deal with the layout. This may include collecting additional information about the concepts in the specification, configuring existing auto-layouting mechanisms, etc.

  Contact: Jakub Klímek

• RDF distiller CSV on the Web

  Parent project: Podpora moderních specifikací pro webová data tvorbou nástrojů

  CSV on the Web je sada doporučení konsorcia W3C - webový standard pro popis tabulkových CSV souborů na Webu. Při správném popisu pak umožňuje na takový CSV soubor pohlížet i jako na propojená data v RDF. Výhodou tohoto přístupu je, že vývojáři zvyklí na CSV vidí CSV, a vývojáři využívající výhody propojených dat vidí RDF. Tématem projektu je implementovat tzv. RDF distiller, který pro vstupní CSV soubor a jeho deskriptor vygeneruje příslušná RDF data.

  Contact: Jakub Klímek

• Bezeztrátové generování CSV on the Web souborů z RDF

  Parent project: Podpora moderních specifikací pro webová data tvorbou nástrojů

  CSV on the Web je sada doporučení konsorcia W3C - webový standard pro popis tabulkových CSV souborů na Webu. Při správném popisu pak umožňuje na takový CSV soubor pohlížet i jako na propojená data v RDF. Pro řadu uživatelů dat je příjemnější pracovat s CSV soubory než s daty v RDF. Tématem projektu je navrhnout, implementovat a otestovat způsob, jak z RDF dat vygenerovat sadu CSV souborů popsaných CSV on the Web tak, aby došlo k co nejmenší ztrátě informace.

  Contact: Jakub Klímek

• Prohlížeč datasetů (pomocí vizualizací)

  Parent project:

  Student se zapojí do týmového vývoje projektu GAČR 22-21696S - Deep Visual Representations of Unstructured Data. Jeho úkolem bude navrhnout a implementovat nástroj pro exploraci a vizualizaci datasetů, např. z platformy Kaggle. Dále bude jeho úkolem navrhnout, implementovat a experimentálně vyhodnotit vizualizaci relačních datatasetů jako celku.

  Contact: David Bernhauer