ShokofehVS/REMBic-BCCA

REMBic-BCCA

This project implements a powerful algorithm for identifying biclusters and co-activation patterns within biological datasets. BCCA can help uncover hidden relationships and functional insights in complex biological data.

Table of Contents

• Introduction
• Features
• Getting Started
  • Installation
  • Usage
• Algorithm Overview
• Evaluation
• Results
• Contributing
• License

Introduction

The BCCA Algorithm is designed to identify groups of genes that exhibit similar expression patterns across various conditions. It employs a biclustering approach to discover subsets of genes and conditions that exhibit co-activation, which can be crucial in understanding complex biological processes.

Features

• Biclustering: Discover patterns of co-activated genes across subsets of conditions.
• Co-Activation Analysis: Identify relationships and interactions among genes.
• Evaluation Metrics: Evaluate the algorithm's performance using precision, recall, F1-score, accuracy, and purity metrics.
• Cross-Validation: Utilize cross-validation for robust evaluation.

Getting Started

Installation

1. Clone the repository:

   git clone git@github.com:kamzon/REMBic-BCCA.git
   cd REMBic-BCCA
   python BCCA_modified.py