The ML_Genomics repository, is a collection of resources and tools focused on the application of machine learning techniques in genomics. It includes various submodules and scripts aimed at facilitating genomic data analysis, feature selection, and predictive modeling.
The repository comprises several submodules, each focusing on different aspects of machine learning in genomics:
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ML-TISCH2-scRNAseq-main: Contains resources and scripts for analyzing single-cell RNA sequencing (scRNA-seq) data using machine learning approaches, leveraging the TISCH2 database.
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biomarkers-main: Offers tools and scripts for identifying and analyzing genomic biomarkers, aiding in the discovery of genetic indicators associated with diseases or traits.
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ml-genomics-resources-main: Provides a curated list of machine learning resources applicable to genomics, including datasets, tutorials, and relevant literature.
To explore the contents of this repository:
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Clone the Repository:
git clone https://github.com/pritampanda15/ML_Genomics.git
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Navigate to a Submodule:
cd ML_Genomics/ML-TISCH2-scRNAseq-main -
Follow Instructions: Each submodule may contain its own README or documentation detailing installation steps, dependencies, and usage instructions.
Contributions to enhance the repository are welcome. To contribute:
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Fork the Repository: Click on the 'Fork' button at the top right corner of the repository page.
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Create a New Branch: For your feature or bug fix.
git checkout -b feature-name
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Make Changes: Implement your feature or fix.
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Commit Changes:
git commit -m "Description of changes" -
Push to Your Fork:
git push origin feature-name
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Submit a Pull Request: Navigate to your forked repository on GitHub and click on 'New Pull Request'.
The repository does not specify a license. It's advisable to contact the repository owner for clarification before using the code in commercial or open-source projects.
For more details, visit the ML_Genomics repository.