Machine Learning for Bioinformatics

These are my solutions to the assignments of the graduate course Machine Learning for Bioinformatics offered by the CE Department at Sharif University in Spring quarter of 2020.

Assignments

1. Heart Disease Data Analysis with KNN and Decision Tree from scratch Codes & Data
2. Analyzing the Sklearn Breast Cancer dataset using Soft Margin SVM and Perceptron algorithms. Codes
3. Analyzing the Sklearn Breast Cancer dataset using Ensemble Algorithms and Feature Selection. Codes
4. Dimensionality Reduction using PCA and Clustering with K-means and GMM on ALL/AML cancer dataset Codes & Data
5. Brain Tumor Classification with ResNet and Feature Map Visualization Codes & Data
6. Implementation of Autoencoder (AE) and Variational Autoencoder (VAE) and Image Interpolation Codes
7. Baum-Welch Algorithm using hidden-markov library Codes
8. Predicting Activity of Compounds for Drug Discovery Codes & Data
9. A simple Multi-Layer Perceptron (MLP) implementation Codes
10. Implemeting an LSTM for ECG Heartbeat Classification Codes

Projects

1. Implementing Simboost for drug-target binding affinity prediction Codes & Data - Based on the paper SimBoost: a read-across approach for predicting drug–target binding affinities using gradient boosting machines
2. Improving DeepDTA model for drug-target binding affinity prediction Codes & Data - Based on the paper DeepDTA: deep drug–target binding affinity prediction