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A Comprehensive Review of the Data and Knowledge Graphs Approaches in Bioinformatics
Computer Science and Information Systems, Tome 21 (2024) no. 3.

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The scientific community is currently showing strong interest in constructing knowledge graphs from heterogeneous domains (genomic, pharmaceutical, clinical etc.). The main goal here is to support researchers in gaining an immediate overview of the biomedical and clinical data that can be utilized to construct and extend KGs. A in-depth overview of the available biomedical data and the latest applications of knowledge graphs, from the biological to the clinical context, is provided showing the most recent methods of representing biomedical knowledge with embeddings (KGEs). Furthermore, this review, differentiates biomedical databases based on their construction process (whether manually curated by experts or not), aiming to offer a detailed overview and guide researchers in selecting the appropriate database for their research considering to the specific project needs, available resources, and data complexity. In conclusion, the review highlights current challenges: integration of different knowledge graphs and the interpretability of predictions of new relations.
Keywords: Biomedical Knowledge Graph, Knowledge Graph Embeddings, Text Mining, Graph Neural Network 
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     title = {A {Comprehensive} {Review} of the {Data} and {Knowledge} {Graphs} {Approaches} in {Bioinformatics}},
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Ylenia Galluzzo. A Comprehensive Review of the Data and Knowledge Graphs Approaches in Bioinformatics. Computer Science and Information Systems, Tome 21 (2024) no. 3. http://geodesic.mathdoc.fr/item/CSIS_2024_21_3_a17/