Name
Abstract | ||
|---|---|---|
Background The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. Results This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results. Conclusions Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1186/s13326-021-00239-z | JOURNAL OF BIOMEDICAL SEMANTICS |
Keywords | DocType | Volume |
Ontology, Semantic enhancement, Gene, Deep learning, Machine learning | Journal | 12 |
Issue | ISSN | Citations |
1 | 2041-1480 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreea Grigoriu | 1 | 0 | 0.34 |
| Amrapali Zaveri | 2 | 368 | 24.37 |
| Gerhard Weiss | 3 | 1100 | 130.79 |
| Michel Dumontier | 4 | 0 | 0.34 |