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GENTLE is a comprehensive application that helps users to generate and analyze features from T cell receptor (TCR) repertoires for machine learning purposes. The application leverages the streamlit framework for an interactive UI and various Python libraries to offer extensive data handling, feature generation, dimensionality reduction, classification, and model validation functionalities. It embodies a powerful platform designed to facilitate the generation and analysis of TCR repertoire features, aiding researchers in improving their understanding and development of machine learning models in immunology.

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Use Cases Limitations Evidence Owner's Insight

The GENTLE application is a versatile tool designed for researchers and data scientists working in immunology, particularly those focused on T cell receptor (TCR) repertoire analysis. Its primary use cases include investigating the diversity of TCR repertoires, generating and analyzing sequence motifs, building feature-rich network models, and applying dimensionality reduction techniques to streamline data. By facilitating comprehensive feature generation and selection, the app aids in the development, evaluation, and validation of predictive models. It is particularly useful for identifying biomarkers, understanding immune responses, and advancing personalized medicine approaches by uncovering patterns and insights embedded within TCR data.

See owner's GitHub repository for more information:

This app is based on the paper:

Andrade, D.S., Terrematte, P., RennĂ³-Costa, C. et al. GENTLE: a novel bioinformatics tool for generating features and building classifiers from T cell repertoire cancer data. BMC Bioinformatics 24, 32 (2023).

This application was not uploaded by the author, but through their publicly available Github repository,


Warning: Not intended for clinical use. Assume outputs are unsafe and unvalidated. Use carefully.

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  • Precision Medicine & Genomics


Kinal Patel

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