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Identifying translational science through embeddings of controlled vocabularies.

ABSTRACT: OBJECTIVE:Translational science aims at "translating" basic scientific discoveries into clinical applications. The identification of translational science has practicality such as evaluating the effectiveness of investments made into large programs like the Clinical and Translational Science Awards. Despite several proposed methods that group publications-the primary unit of research output-into some categories, we still lack a quantitative way to place articles onto the full, continuous spectrum from basic research to clinical medicine. MATERIALS AND METHODS:I learn vector representations of controlled vocabularies assigned to Medline articles to obtain a translational axis that points from basic science to clinical medicine. The projected position of a term on the translational axis, expressed by a continuous quantity, indicates the term's "appliedness." The position of an article, determined by the average location over its terms, quantifies the degree of its appliedness, which I term the level score. RESULTS:I validate the present method by comparing with previous techniques, showing excellent agreement yet uncovering significant variations of scores of articles in previously defined categories. The measure allows us to characterize the standing of journals, disciplines, and the entire biomedical literature along the basic-applied spectrum. Analysis on large-scale citation network reveals 2 main findings. First, direct citations mainly occurred between articles with similar scores. Second, shortest paths are more likely ended up with an article closer to the basic end of the spectrum, regardless of where the starting article is on the spectrum. CONCLUSIONS:The proposed method provides a quantitative way to identify translational science.

SUBMITTER: Ke Q

PROVIDER: S-EPMC7647337 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Identifying translational science through embeddings of controlled vocabularies.

Ke Qing Q

Journal of the American Medical Informatics Association : JAMIA 20190601 6

<h4>Objective</h4>Translational science aims at "translating" basic scientific discoveries into clinical applications. The identification of translational science has practicality such as evaluating the effectiveness of investments made into large programs like the Clinical and Translational Science Awards. Despite several proposed methods that group publications-the primary unit of research output-into some categories, we still lack a quantitative way to place articles onto the full, continuous ...[more]

PMID: 30830170

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Project description:BackgroundCytokines are a class of small proteins that act as chemical messengers and play a significant role in essential cellular processes including immunity regulation, hematopoiesis, and inflammation. As one important family of cytokines, tumor necrosis factors have association with the regulation of a various biological processes such as proliferation and differentiation of cells, apoptosis, lipid metabolism, and coagulation. The implication of these cytokines can also be seen in various diseases such as insulin resistance, autoimmune diseases, and cancer. Considering the interdependence between this kind of cytokine and others, classifying tumor necrosis factors from other cytokines is a challenge for biological scientists.MethodsIn this research, we employed a word embedding technique to create hybrid features which was proved to efficiently identify tumor necrosis factors given cytokine sequences. We segmented each protein sequence into protein words and created corresponding word embedding for each word. Then, word embedding-based vector for each sequence was created and input into machine learning classification models. When extracting feature sets, we not only diversified segmentation sizes of protein sequence but also conducted different combinations among split grams to find the best features which generated the optimal prediction. Furthermore, our methodology follows a well-defined procedure to build a reliable classification tool.ResultsWith our proposed hybrid features, prediction models obtain more promising performance compared to seven prominent sequenced-based feature kinds. Results from 10 independent runs on the surveyed dataset show that on an average, our optimal models obtain an area under the curve of 0.984 and 0.998 on 5-fold cross-validation and independent test, respectively.ConclusionsThese results show that biologists can use our model to identify tumor necrosis factors from other cytokines efficiently. Moreover, this study proves that natural language processing techniques can be applied reasonably to help biologists solve bioinformatics problems efficiently.

Dataset Information

Identifying translational science through embeddings of controlled vocabularies.

Publications

Identifying translational science through embeddings of controlled vocabularies.

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