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SSIF: Subsumption-based Sub-term Inference Framework to audit Gene Ontology

Abeysinghe, Rashmie; Hinderer, Eugene W; Moseley, Hunter N; Cui, Licong (January 2020, Bioinformatics)

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Exploring Deep Learning-based Approaches for Predicting Concept Names in SNOMED CT

https://doi.org/10.1109/BIBM.2018.8621076

Zheng, Fengbo; Cui, Licong (December 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))

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A Lexical Approach to Identifying Subtype Inconsistencies in Biomedical Terminologies

https://doi.org/10.1109/BIBM.2018.8621511

Abeysinghe, Rashmie; Zheng, Fengbo; Hinderer, Eugene W.; Moseley, Hunter N.; Cui, Licong (December 2018, 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))

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Query-constraint-based mining of association rules for exploratory analysis of clinical datasets in the National Sleep Research Resource

https://doi.org/10.1186/s12911-018-0633-7

Abeysinghe, Rashmie; Cui, Licong (July 2018, BMC Medical Informatics and Decision Making)

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An efficient, large-scale, non-lattice-detection algorithm for exhaustive structural auditing of biomedical ontologies

https://doi.org/10.1016/j.jbi.2018.03.004

Zhang, Guo-Qiang; Xing, Guangming; Cui, Licong (April 2018, Journal of Biomedical Informatics)

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Identifying Similar Non-Lattice Subgraphs in Gene Ontology based on Structural Isomorphism and Semantic Similarity of Concept Labels

Abeysinghe, Rashmie; Qu, Xufeng; Cui, Licong (January 2018, AMIA Annual Symposium proceedings)

Non-Lattice Subgraphs (NLSs) are graph fragments of a terminology which violates the lattice property, a desirable property for a well-formed terminology. They have been proven to be useful in identifying inconsistencies in biomedical terminologies. Similar NLSs may denote similar inconsistencies that may suggest possibly similar remediations. Therefore, we investigate a structural-semantic-based approach to identify similar NLSs in the Gene Ontology (GO). For an input NLS, we first obtain all its isomorphic NLSs. Then, we compare each concept of the input NLS with the corresponding concept in an isomorphic NLS and then compute a similarity score for the two NLSs. Applying this approach to 10 different structures of NLSs in GO, we found that 38.43% (910/2368) of NLSs have at least one similar NLS. We also observed some interesting lexical patterns frequently existing in similar NLSs. Our approach may be applicable to other biomedical terminologies for identifying similar NLSs.
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Auditing SNOMED CT hierarchical relations based on lexical features of concepts in non-lattice subgraphs

https://doi.org/10.1016/j.jbi.2017.12.010

Cui, Licong; Bodenreider, Olivier; Shi, Jay; Zhang, Guo-Qiang (December 2017, Journal of Biomedical Informatics)

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Query-constraint-based association rule mining from diverse clinical datasets in the national sleep research resource

https://doi.org/10.1109/BIBM.2017.8217834

Abeysinghe, Rashmie; Cui, Licong (November 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))

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Quality Assurance of NCI Thesaurus by Mining Structural-Lexical Patterns

Abeysinghe, Rashmie; Brooks, Michael A; Talbert, Jeffery; Cui, Licong (November 2017, AMIA Annual Symposium Proceedings)

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Auditing subtype inconsistencies among gene ontology concepts

https://doi.org/10.1109/BIBM.2017.8217835

Abeysinghe, Rashmie; Hinderer, Eugene W.; Moseley, Hunter N.; Cui, Licong (November 2017, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM))

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