aspav / dsit

Projects developed in the scope of the MSc program Data Science & Information Technologies (DSIT)/Bioinformatics-Biomedical Data Science at NKUA

DSIT: Data Science & Information Technologies

This repository contains the projects that were developed in the scope of the courses of the MSc program Data Science and Information Technologies-Bioinformatics & Biomedical Data Science.

©️ License

This program comes with ABSOLUTELY NO WARRANTY. This is free software, and you are welcome to redistribute it under certain conditions; See the GNU General Public License v3 for more details.

📁 AIMB

✏️ Algorithms in Molecular Biology

This folder contains the code and reports for the assignments of the course Algorithms in Molecular Biology as well as the data that are required to run the notebooks.

📁 data 🗄️

📁 notebooks

• 💾 frequent_words.ipynb

• 💾 motif_search_algorithms.ipynb

• 💾 cutoff_heuristic (to be uploaded)

• 💾 miRNA_BUFET (to be uploaded)

📁 AISB

✏️ Algorithms in Structural Bioinformatics

This project contains four notebooks that were developed in the scope of the course Algorithms in Structural Bioinformatics.

📁 data 🗄️

📁 notebooks

• 💾 Implementation of c-RMSD & d-RMSD

• 💾 Implementation of a simplified Zucker minimization algorithm

• 💾 Construction of Cayley-Menger matrix B-rank(B) computation-perturbation

• 💾 Playing with pdb files

📁 reports

• 📝 playing_with_pdb_files.pdf

📁 DSITNeuro

✏️ Application of Data Science and Information Technologies in Neurosciences

This folder contains the code (implemented in matlab) and the reports (in pdf format) for the following assignments of the course:

📁 IntegrateFireNeuron: Integrate and Fire Neuron Model f-I curve

• 💾 IntegrateFireNeuron.m

• 📝 IntegrateFireNeuron.pdf

📁 HodgkinHuxley: The Hodgkin-Huxley Model as an Oscillator

• 💾 HodgkinHuxley.m

• 📝 HodgkinHuxley.pdf

📁 MLCB

✏️ Machine Learning in Computational Biology

🖿 Supervised Machine Learning

• 📁 output

• 💾 ex_3.py

• 💾 ex_4.py

• 💾 ex_5.py

• 📝 Supervised_Learning.pdf

🖿 Unsupervised Machine Learning

• 📁 datasets 🗄️

• 💾 Unsupervised_Learning.ipynb

• 📝 Unsupervised_Learning.pdf

⚙️ HOW TO

🐍 Regarding the python files and notebooks, the next steps should be followed to run them:

✅️️ NOTE: I used Python 3.8 for running the following commands.

1. Install the virtualenv package:

pip install virtualenv

2. Create a virtual environment

virtualenv venv

3. Activate the virtual environment

. vnenv/bin/activate

4. Install the required packages, provided in the requirements.txt file:

pip install -r requirements.txt

5. Running the files.

• To run a jupyter notebook type:

  jupyter notebook

• To run the .py files type:

  python3 *.py

🧮 Regarding the matlab files, you should download MATLAB 9.4 2018a.