BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials
Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and amalgamate them into a uniform format for easier ac...
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Published in | Medical & biological engineering & computing Vol. 58; no. 10; pp. 2339 - 2354 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.10.2020
Springer Nature B.V |
Subjects | |
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Abstract | Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and amalgamate them into a uniform format for easier access by the end-user like biologist, pharmacist, and data scientist. The proposed integrated biomedical knowledge base, BIOINTMED, has 11,299, 12,981, 4428, 61,491, 48,663, and 13,146 unique entities for drugs, diseases, targets, genes, biomedical pathways, and adverse events, respectively. The uniform aggregated collection is also explored to study the interaction among these entity pairs. Finally, a complete statistical analysis of the consolidated biomedical entities is provided. |
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AbstractList | Biomedical data are complex and heterogeneous. An ample reliable quantity of data is important for understanding and exploring the domain. The work aims to integrate biomedical data from various heterogeneous sources like dictionaries or corpus and amalgamate them into a uniform format for easier access by the end-user like biologist, pharmacist, and data scientist. The proposed integrated biomedical knowledge base, BIOINTMED, has 11,299, 12,981, 4428, 61,491, 48,663, and 13,146 unique entities for drugs, diseases, targets, genes, biomedical pathways, and adverse events, respectively. The uniform aggregated collection is also explored to study the interaction among these entity pairs. Finally, a complete statistical analysis of the consolidated biomedical entities is provided. |
Author | Saha, Ankita Sarkar, Sudeshna Mukhopadhyay, Jayanta Gattu, Mahanandeeshwar |
Author_xml | – sequence: 1 givenname: Ankita orcidid: 0000-0003-3925-9370 surname: Saha fullname: Saha, Ankita email: ankitasaha@iitkgp.ac.in – sequence: 2 givenname: Jayanta surname: Mukhopadhyay fullname: Mukhopadhyay, Jayanta – sequence: 3 givenname: Sudeshna surname: Sarkar fullname: Sarkar, Sudeshna – sequence: 4 givenname: Mahanandeeshwar surname: Gattu fullname: Gattu, Mahanandeeshwar |
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Cites_doi | 10.1371/journal.pcbi.1002826 10.1016/j.talanta.2017.04.076 10.1093/nar/gkw943 10.1093/nar/gkq1237 10.1089/adt.2016.742 10.1016/j.knosys.2014.10.001 10.1093/nar/gkj067 10.1093/nar/gku989 10.4018/978-1-59904-252-7.ch010 10.1093/nar/gkm958 10.3389/fphar.2019.00975 10.1093/nar/gkp1002 10.1177/1177625017696074 10.1016/j.drudis.2010.10.003 10.1016/j.vascn.2017.09.141 10.1093/nar/gkv951 10.3174/ajnr.A4696 10.1007/978-3-642-23740-9 10.1093/nar/28.1.27 10.1371/journal.pone.0116656 10.1093/nar/gkv1075 10.1289/txg.6028 |
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Copyright | International Federation for Medical and Biological Engineering 2020 International Federation for Medical and Biological Engineering 2020. |
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DOI | 10.1007/s11517-020-02201-0 |
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Title | BIOINTMED: integrated biomedical knowledge base with ontologies and clinical trials |
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