Skip to content

Implementation of a comparative analysis to find the best ML model for classifying dry eye disease from healthy controls using metabolomics datasets.

License

Notifications You must be signed in to change notification settings

sajadamouei/classification-metabolomics

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

58 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data

Reproduce results: Open In Colab

Overview

pipeline1-1

This repository contains the Jupyter Notebook and data sets used in our study titled "Classifying Dry Eye Disease Patients from Healthy Controls Using Machine Learning and Metabolomics Data." The research aimed to explore and optimize machine learning models for classifying cataract patients with and without Dry Eye Disease using metabolomics data. The metabolomics data sets used in this study are imbalanced, with a small number of samples and a large number of features.

How to use

Click on the Open In Colab Google Colab will open in your web browser, and you may need to sign in with your Google account.
Once you're in Colab, you can run the notebook cells sequentially to reproduce our analysis or modify the code.
Additionally, there is a form where you can select the dataset you wish to analyze and explore using machine learning models.

About

Implementation of a comparative analysis to find the best ML model for classifying dry eye disease from healthy controls using metabolomics datasets.

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published