https://github.com/dimitrisstyl7/bioinformatics-project
The goal of this project is to implement solutions to specific exercises from the "Introduction to Bioinformatics Algorithms" textbook.
https://github.com/dimitrisstyl7/bioinformatics-project
bioinformatics game-theory-algorithms hidden-markov-models python viterbi-algorithm
Last synced: 3 months ago
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The goal of this project is to implement solutions to specific exercises from the "Introduction to Bioinformatics Algorithms" textbook.
- Host: GitHub
- URL: https://github.com/dimitrisstyl7/bioinformatics-project
- Owner: dimitrisstyl7
- License: mit
- Created: 2023-06-24T11:13:59.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2024-11-26T22:25:43.000Z (7 months ago)
- Last Synced: 2025-03-18T02:03:24.833Z (3 months ago)
- Topics: bioinformatics, game-theory-algorithms, hidden-markov-models, python, viterbi-algorithm
- Language: Python
- Homepage:
- Size: 5.28 MB
- Stars: 2
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Bioinformatics (2023) - Multiple Exercises
## Project Overview
This project is a team assignment for the "Bioinformatics" course, offered in the 6th semester of the 2023 academic year at the University of Piraeus, Department of Informatics. The goal of this project is to implement solutions to specific exercises from the "Introduction to Bioinformatics Algorithms" textbook. The assignment is divided into three main exercises, each contributing to the final grade.
## Course Information
- **Institution:** [University of Piraeus](https://www.unipi.gr/en/)
- **Department:** [Department of Informatics](https://cs.unipi.gr/en/)
- **BSc course:** Bioinformatics (2023)
- **Semester:** 6th
## Technologies Used
- Python
## Exercises
### Exercise 11.4
- **Description**: This exercise involves decoding the most probable sequence of states (α/β) for a given sequence GGCT using a Hidden Markov Model (HMM). We utilize logarithmic scores instead of normal probability scores.
### Exercise 6.13
- **Description**: Two players play a game with two sequences of nucleotides of lengths `n` and `m`, respectively. In each round, a player can remove a random number of nucleotides from one or both sequences. The player who removes the last nucleotide wins. In the documentation, we describe the winning strategy for all values of `n` and `m`.
### Exercise 6.14
- **Description**: This exercise involves a game with two sequences of nucleotides of lengths `n` and `m`. Each player can remove two nucleotides from one sequence and one nucleotide from the other in each round. The player who cannot make a move loses. In the documentation, we describe the winning strategy for all values of `n` and `m`.
## Setup Instructions
1. Clone the repository or download the project files.
2. Navigate to the relevant topic directories.
3. Follow the instructions in the documentation to run the code.
## Contributors
Theodoros Koxanoglou
Apostolis Siampanis
Dimitris Stylianou
Konstantinos Loizidis
## Acknowledgments
This project was developed as part of the "Bioinformatics" BSc course at the University of Piraeus. Contributions and feedback are always welcome!
## License
This project is licensed under the MIT License - see the [LICENSE](./LICENSE) file for details.