17,707 research outputs found
Exploring the relationship between the Engineering and Physical Sciences and the Health and Life Sciences by advanced bibliometric methods
We investigate the extent to which advances in the health and life sciences
(HLS) are dependent on research in the engineering and physical sciences (EPS),
particularly physics, chemistry, mathematics, and engineering. The analysis
combines two different bibliometric approaches. The first approach to analyze
the 'EPS-HLS interface' is based on term map visualizations of HLS research
fields. We consider 16 clinical fields and five life science fields. On the
basis of expert judgment, EPS research in these fields is studied by
identifying EPS-related terms in the term maps. In the second approach, a
large-scale citation-based network analysis is applied to publications from all
fields of science. We work with about 22,000 clusters of publications, each
representing a topic in the scientific literature. Citation relations are used
to identify topics at the EPS-HLS interface. The two approaches complement each
other. The advantages of working with textual data compensate for the
limitations of working with citation relations and the other way around. An
important advantage of working with textual data is in the in-depth qualitative
insights it provides. Working with citation relations, on the other hand,
yields many relevant quantitative statistics. We find that EPS research
contributes to HLS developments mainly in the following five ways: new
materials and their properties; chemical methods for analysis and molecular
synthesis; imaging of parts of the body as well as of biomaterial surfaces;
medical engineering mainly related to imaging, radiation therapy, signal
processing technology, and other medical instrumentation; mathematical and
statistical methods for data analysis. In our analysis, about 10% of all EPS
and HLS publications are classified as being at the EPS-HLS interface. This
percentage has remained more or less constant during the past decade
Definitions in ontologies
Definitions vary according to context of use and target audience. They must be made relevant for each context to fulfill their cognitive and linguistic goals. This involves adapting their logical structure, type of content, and form to each context of use. We examine from these perspectives the case of definitions in ontologies
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
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