text2term Ontology Mapping
About text2term Ontology Mapping
The text2term mapping application is designed to be a flexible computational instrument to generate potential ontology mappings for a given list of strings that represent biomedical entities (e.g., diseases, phenotypes, cell types). The tool uses one of multiple (user-selected) similarity metrics to compare the given strings with all terms in an ontology (their labels, synonyms, etc.).
The text2term application can be used in two ways:
- Programmatically, by importing the text2term package from a Python environment, or by using a command-line interface; and
- Interactively, via a web application with user interfaces for input entry, mapping visualization, verification, and download. The text2term web application can be deployed using Docker.
- Python package for programmatic use: https://pypi.org/project/text2term
- Web application for interactive use: https://text2term.hms.harvard.edu
- The interactive web application can be deployed in any machine with Docker (see https://github.com/ccb-hms/ontology-mapper-ui).
- Harvey Mudd College Computer Science Clinic Program Team of 2021/22: An Nguyen, Kobe Rico, Carmen Benitez, Cindy Lay, Matthew Waddell, Prof. Jamie Haddock (Clinic Team Faculty Advisor)
Point of Contact (questions, etc.)
- Backend: https://github.com/ccb-hms/ontology-mapper
- Frontend: https://github.com/ccb-hms/ontology-mapper-ui
- Issue tracker for specific requests (e.g., new features, bug fixes): https://github.com/ccb-hms/ontology-mapper/issues