In many areas of academic publishing, there is an explosion of literature, and sub-division of fields into subfields, leading to stove-piping where sub-communities of expertise become disconnected from each other. This is especially true in the genetics literature over the last 10 years where researchers are no longer able to maintain knowledge of previously related areas. This paper extends several approaches based on natural language processing and corpus linguistics which allow us to examine corpora derived from bodies of genetics literature and will help to make comparisons and improve retrieval methods using domain knowledge via an existing gene ontology. We derived two open access medical journal corpora from PubMed related to psychiatric genetics and immune disorder genetics. We created a novel Gene Ontology Semantic Tagger (GOST) and lexicon to annotate the corpora and are then able to compare subsets of literature to understand the relative distributions of genetic terminology, thereby enabling researchers to make improved connections between them.
The Gene Ontology (GO) project is a collaborative effort to address the need for consistent descriptions of gene products across databases. The go-basic.obo is the basic version of the GO ontology, filtered such that the graph is guaranteed to be acyclic paths, and annotations can be propagated up the graph. We focused on the is_a relation in order to trace ancestors and children for each entry in the ontology. We chose the is_a relationship in the first instance because it has a more intuitive meaning. Something is only considered is_a if an instance of the child process is an instance of the entire parent process. To parse the OBO file we created Java code that combines the use of publicly available OBO library10 with Java Directed Graph (Digraphs) to trace the paths from a node child to the root. The code used Breadth First and Depth First algorithms to quickly and accurately extract the paths. Figure 1 shows an example of a directed graph for the basophil homeostasis GO entry. The figure shows two paths starting from the child entry up to the biological process root.
Our code allowed us to generate a USAS tagger dictionary file where each entry in the OBO ontology is tagged with the GO IDs shown in its path. Taking the “mucosal immune response” OBO entry shown in Figure 1 we can see there are two paths starting from the child node towards the “biological process” root. The dictionary creation process works as follows:
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- determine whether the child node is single word or multi-word expression. The example shows the latter.
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- determine the number of paths towards the root.
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- get each path’s GoID entries (child node’s ancestors)
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- include the level of each ancestor by adding that to the end of each entry (e.g. .1 to refer to the first parent (GOO:0002251).
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- determine whether the path passes through an “immune system process”, which is the one with GoID: 0002376. If so we add .I to the end of the GoID tag to refer to immune entry, other
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Please cite out publications if you end up using our code, thanks:
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El-Haj, M. , Rayson, P., Piao, S., and Knight, Jo.. "Profiling Medical Journal Articles Using a Gene Ontology Semantic Tagger". The 11th edition of the Language Resources and Evaluation Conference (LREC'18). May 2018. Miyazaki, Japan https://www.lancaster.ac.uk/staff/elhaj/docs/706_Paper.pdf
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El-Haj, M. , Nathan Rutherford, Matthew Coole, Ignatius Ezeani, Sheryl Prentice, Nancy Ide, Jo Knight, Scott Piao, John Mariani, Paul Rayson and Keith Suderman "Infrastructure for Semantic Annotation in the Genomics Domain". 12th edition of the Language Resources and Evaluation Conference (LREC'20). May 2020. Marseille, France https://www.lancaster.ac.uk/staff/elhaj/docs/genomics.pdf
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Sheryl Prentice; Jo Knight; Paul Rayson; El-Haj, M. and Nathan Rutherford. " Problematising Characteristicness: A Biomedical Association Case Study". In the IJCL Journal (International Journal of Corpus Linguistics) https://www.lancaster.ac.uk/staff/elhaj/docs/ijcl.19019.pre.pdf
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