Model-Driven Software Development for Scientific Research

Course guarantor:

Lecturer:

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Department of Software Engineering

Synopsis:

Students will acquire skills in working with conceptual models and their application to complex domains within software engineering. The course focuses on the domain of scientific research in bioinformatics. Students will gain an overview of advanced techniques for structural and process modeling using various notations. They will learn to automatically generate model implementations with emphasis on preserving integrity constraints across different technologies (graph and object-relational databases). Students will become familiar with the challenges of modeling and implementing software in bioinformatics. Through projects, they will master procedures for modeling and developing applications in scientific research on a specific domain and gain practical experience in software development.

Requirements:

Syllabus of lectures:

1. Introduction and overview of approaches to structural modeling.

2. Process modeling and integration with data models.

3. Model verification and validation.

4. Introduction to bioinformatics and methods of data representation.

5. Algorithms and data analysis in bioinformatics.

6. Software tools in bioinformatics, publicly available databases, and data sources.

7. Integration of tools in bioinformatics and possibilities of using RDF/SPARQL.

8. Principles of code generation: various approaches to code generation, transformation of models into code.

9. Generation of schemas and code for graph databases.

10. Generation of schemas and code for object-relational databases and preservation of integrity constraints.

11. Principles of converting natural language to SQL queries.

12. Retrieval-augmented generation and its use in software engineering.

Syllabus of tutorials:

Study Objective:

Students will acquire skills in working with conceptual models and their application to complex domains within software engineering. The course focuses on the domain of scientific research in bioinformatics. Students will gain an overview of advanced techniques for structural and process modeling using various notations. They will learn to automatically generate model implementations with emphasis on preserving integrity constraints across different technologies (graph and object-relational databases). Students will become familiar with the challenges of modeling and implementing software in bioinformatics. Through projects, they will master procedures for modeling and developing applications in scientific research on a specific domain and gain practical experience in software development.

Study materials:

1. Guizzardi G.: Ontological Foundations for Structural Conceptual Models. Telematica Instituut Fundamental Research Series, 2005. ISBN 90-75176-81-3.

2. Uhnák P., Pergl R.: The OpenPonk Modeling Platform, in Proc. of the 11th Edition of the International Workshop on Smalltalk Technologies. ACM, New York, USA, 2016. ISBN 978-1-4503-4524-8.

3. Jones N.C., Pevzner P.A.: An Introduction to Bioinformatics Algorithms. The MIT Press, 2004. ISBN 978-0-262-10106-6.

4. Eidhammer I., Flikka K., Martens L., Mikalsen S.O.: Computational Methods for Mass Spectrometry Proteomics. Wiley, 2008. ISBN 978-0-470-51297-5.

5. Rothman D.: RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone. Packt Publishing, 2024. ISBN 978-1-836-20091-8.

6. Gheorghiu A.: Building Data-Driven Applications with LlamaIndex: A practical guide to retrieval-augmented generation (RAG) to enhance LLM applications. Packt Publishing, 2024. ISBN 978-1-835-08950-7.

7. Bouchard L.F., Peters L.: Building LLMs for Production: Enhancing LLM Abilities and Reliability with Prompting, Fine-Tuning, and RAG. Towards AI, 2024. ISBN 979-8-324-73147-2.

Note:

Further information:

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No time-table has been prepared for this course

The course is a part of the following study plans: