
  ## Introduction
  
  This project aims to identify cancer biomarkers using a Python backend (Flask API) and a React frontend, incorporating machine learning capabilities for predictions.
  
  ## Table of Contents
  - [Project Description](#project-description)
  - [Prerequisites](#prerequisites)
  - [Setup Instructions](#setup-instructions)
    - [1. Clone the Repository](#1-clone-the-repository)
    - [2. Setting Up the Backend (Python and Flask)](#2-setting-up-the-backend-python-and-flask)
    - [3. Setting Up the Frontend (React)](#3-setting-up-the-frontend-react)
  
  ## Project Description
  
  This project provides an interface for identifying potential cancer biomarkers. It uses a Python backend powered by Flask, where the machine learning models are hosted, and a React frontend that allows users to interact with the model and view results.
  
  ## Prerequisites
  
  - **Node.js and npm** (for frontend)
  - **Python 3 and pip** (for backend)
  - `virtualenv` (recommended for managing Python dependencies)
  
  ## Setup Instructions
  
  ### 1. Clone the Repository
  
  Clone the repository to your local machine and navigate into the project directory:
  
  ```bash
  git clone https://github.com/AnishMane/Cancer-Associated-Biomarker-Identification.git
  cd Cancer-Associated-Biomarker-Identification
  ```
  
  ### 2. Setting Up the Backend (Python and Flask)
  
  ```bash
  cd backend
  python -m venv venv   # (or `python3 -m venv venv` for Mac)
  venv/Scripts/activate  # (or `source venv/bin/activate` for Mac)
  pip install -r requirements.txt
  ```
  
  ### 3. Setting Up the Frontend (React)
  
  ```bash
  cd frontend
  npm install
  npm start
  ```
    

This project aims to identify cancer biomarkers using a Python backend (Flask API) and a React frontend, incorporating machine learning capabilities for predictions.