Journal article
Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach
PloS one, v 11(5), pp e0156091-e0156091
2016
PMID: 27195695
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In biomedicine, scientific literature is a valuable source for knowledge discovery. Mining knowledge from textual data has become an ever important task as the volume of scientific literature is growing unprecedentedly. In this paper, we propose a framework for examining a certain disease based on existing information provided by scientific literature. Disease-related entities that include diseases, drugs, and genes are systematically extracted and analyzed using a three-level network-based approach. A paper-entity network and an entity co-occurrence network (macro-level) are explored and used to construct six entity specific networks (meso-level). Important diseases, drugs, and genes as well as salient entity relations (micro-level) are identified from these networks. Results obtained from the literature-based literature mining can serve to assist clinical applications.
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Details
- Title
- Identifying Liver Cancer and Its Relations with Diseases, Drugs, and Genes: A Literature-Based Approach
- Creators
- Yongjun Zhu - College of Computing and Informatics, Drexel University, Philadelphia, PA, United States of AmericaMin Song - Department of Library and Information Science, Yonsei University, Seoul, Republic of KoreaErjia Yan - College of Computing and Informatics, Drexel University, Philadelphia, PA, United States of America
- Publication Details
- PloS one, v 11(5), pp e0156091-e0156091
- Publisher
- Public LIbrary of Science (PLOS); United States
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000376291100157
- Scopus ID
- 2-s2.0-84971224860
- Other Identifier
- 991014878323104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Mathematical & Computational Biology