Fig 1.
(a) Knowledge graph web interface queries information from bioprocess knowledge graph database (BKGD) (b) BKGD is developed using information from text-mining, manually curated dictionary concepts and ontology (c) Automated extraction of relations from literature. Created in BioRender. Anandakrishnan, M. (2025) https://BioRender.com/e88e84c.
Fig 2.
Input fields of the Knowledge Graph Web Interface.
Fig 3.
Illustration of the BKGD model with an example of an impact relationship
(a) A visual representation of our graph data model showing entities and the different relationship types (b) An example relationship from BKGD showing a link between an Affector (manganese) and an Affected (afucosylation) (c) An example Dictionary Concept from BKGD with links to its variant terms.
Fig 4.
An example of hierarchical relationships in our ontology.
Disaccharide is one of the lowest classes in the hierarchy of secondary raw materials. It is linked to dictionary concepts (sucrose, maltose, lactose and trehalose) with the ‘INSTANCE_OF’ relationship.
Fig 5.
Overview of the Text-Mining pipeline showing the process of mining an article to extract relation information for the BKGD.
Fig 6.
The results of three different search parameters.
(a) Example where a relation from the same article and two different sentences are linked. (a.1) subject is populated with the Affector term, ‘manganese’; (a.2) object is populated with the Affected term, ‘ADCC’; (a.3) a degree of separation value is selected; (a.4) search results show a link between manganese and ADCC via afucosylation; (a.5) the article evidence supporting the links are displayed (as both the relations are extracted from the same article, the same PMID is displayed twice); (a.6) graph view displays the connections between the entities; (a.7) On mouseover of the first instance of the PMID, the sentence supporting correlation of afucosylation and manganese is displayed; (a.8) On mouseover of the second instance, sentence source of the relation between afucosylation and ADCC is displayed. (b) Example where relations from two different articles are linked. (b.1) The impact relationship field is limited to two types – ‘Positively Correlated’ and ‘Negatively Correlated’; (b.2) the second relation (core fucosylation negatively regulates ADCC) is supported by two distinct articles and the first relation is extracted from another article. (c) Example of search result at ontology level. (c.1) ‘trace metals/minerals’ is selected as the subject class; (c.2) ‘glycosylation’ is selected as the object class; (c.3) results include multiple subject and object terms, where all the subject and object terms belong to the selected classes; (c.4) graph view provides a consolidated view of the links between the resulting entities. Created in BioRender. Anandakrishnan, M. (2025) https://BioRender.com/bh7detl.
Fig 7.
Statistics of data in our Bioprocess knowledge graph database.
This includes graph component statistics, node statistics, relationship statistics, top 5 ontological concepts, top 5 dictionary concepts, top 5 terms, top 5 journal years, and top 5 journal names.
Fig 8.
Illustration of an annotation in TeamTat.
Abstracts are opened in the TeamTat interface where annotators curate sentences with impact relations. The Affector and Affected terms are marked in blue and yellow colors. The relations are established with red dotted lines. The impact relation types and the impacted arguments are also visible in the right side of the window.
Table 1.
Performance Evaluation of the IRES on the annotated abstracts.
Fig 9.
Performance across relation types.
Relation-type-specific evaluation of IRES showing precision (%), recall (%), and F1-score (%) for four correlation categories extracted from the annotated abstracts.