krishnanlab/txt2onto
Code for classifying unstructured text to tissue ontology terms using natural language processing and machine learning.
This tool helps scientists and researchers systematically categorize genomic samples based on their descriptive text, even when the descriptions are inconsistent or non-standard. You input raw, unstructured text descriptions of biological samples, and it outputs standardized tissue and cell-type annotations from a structured ontology like UBERON, along with prediction probabilities. It's designed for anyone managing or analyzing large datasets of human genomic samples.
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Use this if you need to standardize and enrich your genomic sample metadata with precise tissue and cell-type annotations from free-text descriptions.
Not ideal if your samples lack any descriptive text metadata or if you require annotations for tissues/cell types not covered by the 346 UBERON terms included.
Stars
27
Forks
7
Language
Python
License
BSD-3-Clause
Category
Last pushed
Aug 21, 2024
Commits (30d)
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