Use artificial neural network to align biological ontologies

BMC Med Inform Decis Mak

et al. 2019

Self Cite

BackgroundThe medical community uses a variety of data standards for both clinical and research reporting needs. ISO 11179 Common Data Elements (CDEs) represent one such standard that provides robust data point definitions. Another standard is the Biomedical Research Integrated Domain Group (BRIDG) model, which is a domain analysis model that provides a contextual framework for biomedical and clinical research data. Mapping the CDEs to the BRIDG model is important; in particular, it can facilitate mapping the CDEs to other standards. Unfortunately, manual mapping, which is the current method for creating the CDE mappings, is error-prone and time-consuming; this creates a significant barrier for researchers who utilize CDEs.MethodsIn this work, we developed a semi-automated algorithm to map CDEs to likely BRIDG classes. First, we extended and improved our previously developed artificial neural network (ANN) alignment algorithm. We then used a collection of 1284 CDEs with robust mappings to BRIDG classes as the gold standard to train and obtain the appropriate weights of six attributes in CDEs. Afterward, we calculated the similarity between a CDE and each BRIDG class. Finally, the algorithm produces a list of candidate BRIDG classes to which the CDE of interest may belong.ResultsFor CDEs semantically similar to those used in training, a match rate of over 90% was achieved. For those partially similar, a match rate of 80% was obtained and for those with drastically different semantics, a match rate of up to 70% was achieved.DiscussionOur semi-automated mapping process reduces the burden of domain experts. The weights are all significant in six attributes. Experimental results indicate that the availability of training data is more important than the semantic similarity of the testing data to the training data. We address the overfitting problem by selecting CDEs randomly and adjusting the ratio of training and verification samples.ConclusionsExperimental results on real-world use cases have proven the effectiveness and efficiency of our proposed methodology in mapping CDEs with BRIDG classes, both those CDEs seen before as well as new, unseen CDEs. In addition, it reduces the mapping burden and improves the mapping quality.

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using an artificial neural network to map cancer common data elements to the biomedical research integrated domain group model in a semi-automated manner

Renner

BMC Med Inform Decis Mak

et al. 2019

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“…2) We utilized the OpenNLP Library [37] to process all these abstracts and obtained a total of 488,576 nouns and noun phrases. 3) All nouns and noun phrases were mapped with existing OMIT terms or relations through an ontologyalignment tool developed in one of our previous investigations [38]. 4) All unmatched nouns or noun phrases were treated as candidate terms and sorted by their cumulative frequencies among all abstracts.…”

Section: A Term-from-pubmed Modulementioning

confidence: 99%

A semantic approach for knowledge capture of MIcroRNA-Target gene interactions

2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

Gutiérrez

Dou

et al. 2015

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Abstract-Research has indicated that microRNAs (miRNAs), a special class of non-coding RNAs (ncRNAs), can perform important roles in different biological and pathological processes. miRNAs' functions are realized by regulating their respective target genes (targets). It is thus critical to identify and analyze miRNA-target interactions for a better understanding and delineation of miRNAs' functions. However, conventional knowledge discovery and acquisition methods have many limitations. Fortunately, semantic technologies that are based on domain ontologies can render great assistance in this regard. In our previous investigations, we developed a miRNA domain-specific application ontology, Ontology for MIcroRNA Target (OMIT), to provide the community with common data elements and data exchange standards in the miRNA research. This paper describes (1) our continuing efforts in the OMIT ontology development and (2) the application of the OMIT to enable a semantic approach for knowledge capture of miRNA-target interactions.

“…SOCCER [22] is a learning-based approach to reconcile ontologies. Unlike most of other approaches, SOCCER's learning process depends on ontology schema information alone.…”

Section: B Related Workmentioning

confidence: 99%

Ontology-based knowledge discovery and sharing in bioinformatics and medical informatics: A brief survey

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery

Dou

Hayes

et al. 2010

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Abstract-Worldwide health scientists are producing, accessing, analyzing, integrating, and storing massive amounts of digital medical data daily, through observation, experimentation, and simulation. If we were able to effectively transfer and integrate data from all possible resources, then a deeper understanding of all these data sets and better exposed knowledge, along with appropriate insights and actions, would be granted. Unfortunately, in many cases, the data users are not the data producers, and they thus face challenges in harnessing data in unforeseen and unplanned ways. In order to obtain the ability to integrate heterogeneous data, and thereby efficiently revolutionize the traditional medical and biological research, new methodologies built upon the increasingly pervasive cyberinfrastructure are required to conceptualize traditional medical and biological data, and acquire the "deep" knowledge out of original data thereafter. As formal knowledge representation models, ontologies can render invaluable help in this regard. In this paper, we summarize the state-of-the-art research in ontological techniques and their innovative application in medical and biological areas.