Gilda: Grounding Integrating Learned Disambiguation

Gilda is a Python package and REST service that grounds (i.e., finds appropriate identifiers in various namespaces for) named entities in biomedical text.

Gyori BM, Hoyt CT, Steppi A (2022). Gilda: biomedical entity text normalization with machine-learned disambiguation as a service. Bioinformatics Advances, 2022; vbac034 https://doi.org/10.1093/bioadv/vbac034.

Installation

Gilda is deployed as a web service at http://grounding.indra.bio/ (see Usage instructions below), however, it can also be used locally as a Python package.

The recommended method to install Gilda is through PyPI as

pip install gilda

Note that Gilda uses a single large resource file for grounding, which is automatically downloaded into the ~/.data/gilda/<version> folder during runtime (see pystow for options to configure the location of this folder).

Given some additional dependencies, the grounding resource file can also be regenerated locally by running python -m gilda.generate_terms.

Documentation and notebooks

Documentation for Gilda is available here. We also provide several interactive Jupyter notebooks to help use and customize Gilda:

• Gilda Introduction provides an interactive tutorial for using Gilda.
• Custom Grounders shows several examples of how Gilda can be instantiated with custom grounding resources.
• Model Training provides interactive sample code for training new disambiguation models.

Usage

Gilda can either be used as a REST web service or used programmatically via its Python API. An introduction Jupyter notebook for using Gilda is available at https://github.com/indralab/gilda/blob/master/notebooks/gilda_introduction.ipynb

Use as a Python package

For using Gilda as a Python package, the documentation at http://gilda.readthedocs.org provides detailed descriptions of each module of Gilda and their usage. A basic usage example for named entity normalization (NEN), or grounding is as follows:

import gilda
scored_matches = gilda.ground('ER', context='Calcium is released from the ER.')

Gilda also implements a simple dictionary-based named entity recognition (NER) algorithm that can be used as follows:

import gilda
results = gilda.annotate('Calcium is released from the ER.')

Use as a web service

The REST service accepts POST requests with a JSON header on the /ground endpoint. There is a public REST service running at http://grounding.indra.bio but the service can also be run locally as

python -m gilda.app

which, by default, launches the server at localhost:8001 (for local usage replace the URL in the examples below with this address).

Below is an example request using curl:

curl -X POST -H "Content-Type: application/json" -d '{"text": "kras"}' http://grounding.indra.bio/ground

The same request using Python's request package would be as follows:

import requests
requests.post('http://grounding.indra.bio/ground', json={'text': 'kras'})

The web service also supports multiple inputs in a single request on the ground_multi endpoint, for instance

import requests
requests.post('http://grounding.indra.bio/ground_multi',
              json=[
                  {'text': 'braf'},
                  {'text': 'ER', 'context': 'endoplasmic reticulum (ER) is a cellular component'}
              ]
          )

Resource usage

Gilda loads grounding terms into memory when first used. If memory usage is an issue, the following options are recommended.

1. Run a single instance of Gilda as a local web service that one or more other processes send requests to.

2. Create a custom Grounder instance that only loads a subset of terms appropriate for a narrow use case.

3. Gilda also offers an optional sqlite back-end which significantly decreases memory usage and results in minor drop in the number of strings grounder per unit time. The sqlite back-end database can be built as follows with an optional [db_path] argument, which if used, should use the .db extension. If not specified, the .db file is generated in Gilda's default resource folder.

python -m gilda.resources.sqlite_adapter [db_path]

A Grounder instance can then be instantiated as follows:

from gilda.grounder import Grounder
gr = Grounder(db_path)
matches = gr.ground('kras')

Run web service with Docker

After cloning the repository locally, you can build and run a Docker image of Gilda using the following commands:

$ docker build -t gilda:latest .
$ docker run -d -p 8001:8001 gilda:latest

Alternatively, you can use docker-compose to do both the initial build and run the container based on the docker-compose.yml configuration:

$ docker-compose up

Default grounding resources

Gilda is customizable with terms coming from different vocabularies. However, Gilda comes with a default set of resources from which terms are collected (almost 2 million entries as of v1.1.0), without any additional configuration needed. These resources include:

• HGNC (human genes)
• UniProt (human and model organism proteins)
• FamPlex (human protein families and complexes)
• CHeBI (small molecules, metabolites, etc.)
• GO (biological processes, molecular functions, complexes)
• DOID (diseases)
• EFO (experimental factors: cell lines, cell types, anatomical entities, etc.)
• HP (human phenotypes)
• MeSH (general: diseases, proteins, small molecules, cell types, etc.)
• Adeft (misc. terms corresponding to ambiguous acronyms)

Citation

@article{gyori2022gilda,
    author = {Gyori, Benjamin M and Hoyt, Charles Tapley and Steppi, Albert},
    title = "{{Gilda: biomedical entity text normalization with machine-learned disambiguation as a service}}",
    journal = {Bioinformatics Advances},
    year = {2022},
    month = {05},
    issn = {2635-0041},
    doi = {10.1093/bioadv/vbac034},
    url = {https://doi.org/10.1093/bioadv/vbac034},
    note = {vbac034}
}

Funding

The development of Gilda was funded under the DARPA Communicating with Computers program (ARO grant W911NF-15-1-0544) and the DARPA Young Faculty Award (ARO grant W911NF-20-1-0255).