ChexMix: a Content Extraction Tool for Bioentities in Literature

Biomedical databases grow by more than a thousand new publications every day. The large volume of biomedical literature which are being published at an unprecedented rate hinders discovering the relevant knowledge from keywords of interest, getting insights and forming hypotheses. A text-mining tool, PubTator, helps automatically annotate bioentities, such as species, chemicals, genes, and diseases, in PubMed abstracts and PMC full-text articles. However, the re-organization and analysis of bioentities by the researchers is a non-trivial and highly time-consuming task. ChexMix was designed for the extraction of the unique identifiers corresponding to bioentities in query results. In the study, we used ChexMix to construct a taxonomic tree with allied species in Korean native plants or extract MeSH identifiers for the bioentities which co-occurred with the keywords in the same literature. We applied ChexMix to discover allied species related to a keyword of interest and experimentally proved the usefulness of ChexMix or multi species.

Installation

Download data files

Use the download scripts under scripts:

cd scripts
./download_mesh.sh
./download_taxonomy.sh

Development

Install chexmix via pip for dev:

pip install -e <path to chexmix>

Test

Make sure you have tox. If not, run pip tox.

For unitests, run tox.

Launch Docker

Often it is convenient to work in a reproducible environment by docker, and Jupyter docker is useful for it. To launch a docker container with the image:

docker run --rm -p 8888:8888 -v "$PWD":/home/jovyan/chemix -e JUPYTER_ENABLE_LAB=yes jupyter/datascience-notebook:latest

or use docker-compose

docker-compose up

Usage

There are 4 graph types inherited from class BioGraph that includes the following relationships:

Graph types	Relationships
PubMedGraph	Keyword -[MENTIONED] -> Article
PubTatorGraph	Bioentity -[APPEARED_IN]-> Article
MeSHGraph	Bioentity -[INCLUDES]-> Bioentity
TaxonomyGraph	Bioentity -[INCLUDES]-> Bioentity

One may import relevant classes as follows:

from chexmix.graph import BioGraph, TaxonomyGraph, MeSHGraph, PubTatorGraph, PubMedGraph, NodeType, EdgeType, TaxParentType

A graph can be created with node and edge data.

nodes = [node1, node2]
edges = [(node1, node2)]
bio_graph = BioGraph(nodes, edges)

Also each graph has own specific factory methods; PubMedGraph may be built from a search keyword, PubTatorGraph from publication ids, and TaxonomyGraph and MeSHGraph are from the root ids of Taxonomy and MeSH, respectively:

pubmed_graph = PubMedGraph.from_keyword('wnt5a')
pubtator_graph = PubTatorGraph.from_article_ids([3300312, 3300313])
tax_graph = TaxonomyGraph.from_root_ids([9606])
mesh_graph = MeSHGraph.from_root_ids(['D001234', 'D005678'])

Basic Graph Opeartion

One may manipulate BioGraphs using various operations to get some interesting insights:

method name	args	type	description
intersection	other	BioGraph	returns the intersection graph of self with other
difference	other	BioGraph	returns a graph with the nodes that exist in self but not in other
union	other	BioGraph	returns a union graph of two graphs
find_roots	edge_types	Optional[List[EdgeType]]	find root nodes w.r.t. edge_types
find_leaves	edge_types	Optional[List[EdgeType]]	find leaf nodes w.r.t. edge_types
remain_by_node_types	node_types	List[NodeType]	returns a graph that has nodes with node_types
remain_by_edge_types	edge_types	List[EdgeType]	returns a graph with edges of type edge_types
subgraph_from_root	root_node edge_types	str Optional[List[EdgeType]]	returns a subgraph of self including nodes connected with root by edges of edge_types
total_count	nodes	List[NodeId]	returns the sum of the attribute count of nodes
threshold	threshold	int	returns a graph with nodes of which attribute count is larger than threshold
inherit_attr_from	other	BioGraph	return an identical graph with updated node attributes from other if the node also exists in self
set_attribute	attr_key attr_value nodes	str Any List	update node attributes

Example

For a simple demonstration, we build a graphs with Taxonomy and MeSH associated with a given keyword:

# get publications associated with 'keyword' and their publication 'Id's 
pubmed_graph = PubMedGraph.from_keyword(keyword)
article_ids = pubmed_graph.get_article_ids()

# construct two graphs with bioentities of 'MeSH' and 'Taxonomy' mentioned in the publications
pubtator_graph = PubTatorGraph.from_article_ids(article_ids)
bioentities = pubtator_graph.get_bioentities('TAXO')
taxo_graph = TaxonomyGraph.from_pubtator_bioentities(TaxParentType.Genus, bioentities)
bioentities = pubtator_graph.get_bioentities('MESH')
mesh_graph = MeSHGraph.from_pubtator_bioentities(bioentities, ['D', 'C'])  # we focus only chemicals and diseases.

# Take the root nodes from 'mesh_graph' and 'taxonomy_graph', and build a summary graph that contains both root nodes and associated `keyword`
summary_graph = BioGraph().add_edges_from([(keyword, root_node) for root_node in taxo_graph.find_roots()])
mesh_entities_graph = mesh_graph.intersection(pubtator_graph)
summary_graph.add_edges_from([(keyword, appeared_node) for appeared_node in mesh_entities_graph.nodes()])
summary_graph.nodes[keyword]['type'] = "Keyword"
summary_graph.inherit_attr_from(taxo_graph.union(mesh_graph))