Title |
Generation and application of drug indication inference models using typed network motif comparison analysis
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Published in |
BMC Medical Informatics and Decision Making, April 2013
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DOI | 10.1186/1472-6947-13-s1-s2 |
Pubmed ID | |
Authors |
Jaejoon Choi, Kwangmin Kim, Min Song, Doheon Lee |
Abstract |
As the amount of publicly available biomedical data increases, discovering hidden knowledge from biomedical data (i.e., Undiscovered Public Knowledge (UPK) proposed by Swanson) became an important research topic in the biological literature mining field. Drug indication inference, or drug repositioning, is one of famous UPK tasks, which infers alternative indications for approved drugs. Many previous studies tried to find novel candidate indications of existing drugs, but these works have following limitations: 1) models are not fully automated which required manual modulations to desired tasks, 2) are not able to cover various biomedical entities, and 3) have inference limitations that those works could infer only pre-defined cases using limited patterns. To overcome these problems, we suggest a new drug indication inference model. |
X Demographics
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United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
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Brazil | 1 | 3% |
Unknown | 30 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 6 | 19% |
Student > Ph. D. Student | 6 | 19% |
Researcher | 4 | 13% |
Student > Bachelor | 3 | 10% |
Other | 2 | 6% |
Other | 6 | 19% |
Unknown | 4 | 13% |
Readers by discipline | Count | As % |
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Computer Science | 8 | 26% |
Agricultural and Biological Sciences | 5 | 16% |
Medicine and Dentistry | 5 | 16% |
Social Sciences | 3 | 10% |
Pharmacology, Toxicology and Pharmaceutical Science | 2 | 6% |
Other | 4 | 13% |
Unknown | 4 | 13% |