Multiomic Data Classification Using Machine Learning
This repository contains the implementation of a machine learning pipeline for classifying multiomic data. The project leverages various omic datasets (e.g., genomics, transcriptomics, proteomics) to build robust models for identifying patterns and making predictions.
Key Features:
Multiomic Data Integration: Combines multiple layers of omic data (genomics, transcriptomics, etc.) to enhance classification accuracy. Preprocessing and Feature Engineering: Handles preprocessing steps such as normalization, imputation, and dimensionality reduction for optimal model performance. Machine Learning Models: Implements various machine learning algorithms to classify the integrated omic data. Model Evaluation: Provides comprehensive model evaluation using metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Reproducible Workflow: All code is well-documented and organized to allow easy reproducibility of results. This project demonstrates the potential of multiomic data for improving classification tasks in bioinformatics and provides a framework for similar analyses.
