Artificial Intelligence and Machine Learning in Bioinformatics Lab

List of Experiments / Activities

1. BIOINFORMATICS LAB
2. 1Introduction to Python programming and Jupyter Notebook/Google Colab.
3. 2Study of Python libraries: NumPy, Pandas, Matplotlib, and Scikit-learn.
4. 3Loading, exploring, and visualizing biological datasets using Pandas and Matplotlib.
5. 4Data pre-processing: handling missing values, feature scaling, and splitting data into training and testing sets.
6. 5Implementation of Linear Regression and Logistic Regression using Scikit-learn.
7. 6Implementation of Decision Tree and Random Forest for classification.
8. 7Implementation of K-Nearest Neighbour (KNN) and Support Vector Machine (SVM) for classification.
9. 8Implementation of K-Means Clustering and visualization of clustered data.
10. 9Model performance evaluation using Accuracy, Precision, Recall, F1-score, and Confusion Matrix.
11. 10Case study: Disease prediction using a healthcare dataset.
12. 11Case study: Gene expression or DNA sequence classification using a publicly available bioinformatics dataset.
13. Note: The instructor may add / delete / modify / tune experiments as required. Dr. A.P.J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh