Microbiome Interaction using Graph Neural Networks

[psanyalaich]

Deep Learning Simplified Repository (Proposing new issue)

:red_circle: Project Title : Understanding Microbial Interactions Using Graph Neural Networks
:red_circle: Aim : The aim of this project is to leverage Graph Neural Networks (GNNs) to model and analyze the complex interactions between different microbial species within the microbiome. By understanding these interactions, we can gain insights into how microbial networks influence health outcomes and inform strategies for microbiome modulation.
:red_circle: Dataset : The dataset will consist of microbial composition data (e.g., 16S rRNA sequencing data) alongside metadata that includes health outcomes, patient demographics, and environmental factors. The data will be collected from the following publicly available microbiome databases:

• Human Microbiome Project (HMP)
• Metagenomics Rapid Annotation using Subsystem Technology (MG-RAST)
• National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA)
• European Nucleotide Archive (ENA)
  

:red_circle: Approach :

• Conduct an exploratory data analysis (EDA) to understand the dataset’s characteristics, identify patterns, and visualize relationships between microbial species and health outcomes.
• Implement multiple algorithms, focusing on Graph Neural Networks, but also considering traditional models like Random Forest and Support Vector Machines for comparison.
• Evaluate and compare the performance of all models using accuracy scores, F1-scores, and ROC-AUC metrics to determine the best-fit algorithm for modeling microbial interactions.

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📍 Follow the Guidelines to Contribute in the Project :

• You need to create a separate folder named as the Project Title.
• Inside that folder, there will be four main components.
  • Images - To store the required images.
  • Dataset - To store the dataset or, information/source about the dataset.
  • Model - To store the machine learning model you've created using the dataset.
  • requirements.txt - This file will contain the required packages/libraries to run the project in other machines.
• Inside the Model folder, the README.md file must be filled up properly, with proper visualizations and conclusions.

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:red_circle::yellow_circle: Points to Note :

• The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
• "Issue Title" and "PR Title should be the same. Include issue number along with it.
• Follow Contributing Guidelines & Code of Conduct before start Contributing.

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:white_check_mark: To be Mentioned while taking the issue :

• Full name : Prisha Sanyal Aich
• GitHub Profile Link : https://github.com/psanyalaich
• Email ID : prisha.aich@gmail.com
• Participant ID (if applicable): n/a
• Approach for this Project : Use GNNs to model microbial interactions, compare with other algorithms, and analyze results through EDA and accuracy metrics.
• What is your participant role? gssoc-ext, hacktoberfest-accepted

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Happy Contributing 🚀

All the best. Enjoy your open source journey ahead. 😎

[github-actions[bot]]

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