Medical Electronics · Biomedical Data Analysis · Machine Learning · Healthcare Software
Hello world, I'm Barbora!
I am a Bioinformatics and Data Science student with a background in medical electronics, biomedical data analysis and software development.
This GitHub profile serves as my academic and technical portfolio. I use it to collect selected school projects, assignments and research-related work that demonstrate my skills in programming, data analysis, machine learning, bioinformatics.
My main interests are healthcare data, clinical decision support, bioinformatics pipelines, genomic variant analysis, biomedical signal processing and digital health applications.
This portfolio is focused on projects that show my ability to work with:
- biomedical and healthcare data
- Python-based data analysis
- machine learning and statistical evaluation
- genomic variant data
- signal processing
- software tools for medical and research-oriented applications
- technical documentation and structured project work
| Project | Area | Status |
|---|---|---|
| AI Agent for Othello | Artificial Intelligence | in progress |
| Variant Database Comparison Tool | Bioinformatics / Genomic Data | 04/2026 |
| Low-Level MCU Programming | Embedded Systems | 04/2026 |
| Data Analysis of Adherence with Schizophrenia Patient | Data Analysis | 05/2025 |
Area: Artificial Intelligence, game strategy
Period: 05/2026 – in progress
Role: Student developer
The aim of this project is to design and implement an AI agent capable of playing Othello using decision-making strategies and game-state evaluation.
The project involves implementing the game logic, representing possible moves and evaluating strategies for selecting optimal actions. It focuses on practical AI concepts, algorithmic thinking and decision-making under changing conditions.
The project is currently in progress and will be finished furing June 2026. It should demonstrate my ability to work with algorithmic problem solving, structured code and AI-based decision processes.
Python · artificial intelligence · algorithms · game logic · decision-making
Area: Bioinformatics, genomic variant analysis
Context: Extension on the Delfos platform
Period: 04/2026
Role: Student developer / bioinformatics-oriented software work
Genomic variant data can be stored and interpreted across multiple databases and resources. The goal of this project was to support comparison of variant information and make the analysis process more structured and accessible.
The project focused on working with variant-related data and designing a tool that could support comparison across different sources. The work involved understanding the structure of variant data, identifying relevant attributes and preparing logic for database comparison.
The result was a variant database comparison-oriented tool designed as an extension of the Delfos platform. The project connects my interests in bioinformatics, data processing and practical software development for research or clinical-support environments.
bioinformatics · variant analysis · data processing · software development · technical documentation
Area: Embedded systems, microcontroller programming
Period: 04/2026
Role: Student developer
The goal of these projects was to understand low-level embedded programming concepts and work closer to hardware.
The work included basic microcontroller-oriented programming tasks, understanding hardware constraints and writing code for embedded applications.
The repository contains basic low-level MCU programming exercises and examples. These projects show my background in medical electronics and my ability to work with hardware-oriented programming concepts.
C · embedded systems · microcontrollers · low-level programming · hardware-oriented thinking
Area: Healthcare data analysis, digital health
Period: 05/2025
Role: Bachelor thesis author
Long-term digital monitoring systems can provide valuable information about patient behavior and symptom reporting. The aim of my bachelor thesis was to analyze adherence in patients with schizophrenia using data from a digital symptom-monitoring system.
The thesis involved data preprocessing, exploratory data analysis, statistical evaluation and machine learning-based approaches. I worked with patient-related data, adherence metrics and classification models.
The project resulted in a structured analysis of adherence patterns in long-term digital monitoring. It strengthened my experience with healthcare data, Python-based analysis, statistical thinking and interpretation of results in a medical context.
Python · Pandas · data analysis · healthcare data · machine learning · logistic regression · AUC · statistical testing
Email: besbara@seznam.cz
Mobile: +34632330839
GitHub: github.com/barbora-besedova
LinkedIN: https://www.linkedin.com/in/barbora-besedova