Title |
Structuring and extracting knowledge for the support of hypothesis generation in molecular biology
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Published in |
BMC Bioinformatics, October 2009
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DOI | 10.1186/1471-2105-10-s10-s9 |
Pubmed ID | |
Authors |
Marco Roos, M Scott Marshall, Andrew P Gibson, Martijn Schuemie, Edgar Meij, Sophia Katrenko, Willem Robert van Hage, Konstantinos Krommydas, Pieter W Adriaans |
Abstract |
Hypothesis generation in molecular and cellular biology is an empirical process in which knowledge derived from prior experiments is distilled into a comprehensible model. The requirement of automated support is exemplified by the difficulty of considering all relevant facts that are contained in the millions of documents available from PubMed. Semantic Web provides tools for sharing prior knowledge, while information retrieval and information extraction techniques enable its extraction from literature. Their combination makes prior knowledge available for computational analysis and inference. While some tools provide complete solutions that limit the control over the modeling and extraction processes, we seek a methodology that supports control by the experimenter over these critical processes. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 1 | 33% |
France | 1 | 33% |
United States | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 2 | 67% |
Members of the public | 1 | 33% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 5 | 7% |
Brazil | 3 | 4% |
United States | 3 | 4% |
Mexico | 3 | 4% |
United Kingdom | 2 | 3% |
Sweden | 1 | 1% |
Finland | 1 | 1% |
Unknown | 53 | 75% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 21 | 30% |
Student > Master | 9 | 13% |
Student > Ph. D. Student | 8 | 11% |
Professor > Associate Professor | 6 | 8% |
Student > Bachelor | 5 | 7% |
Other | 13 | 18% |
Unknown | 9 | 13% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 23 | 32% |
Computer Science | 22 | 31% |
Medicine and Dentistry | 5 | 7% |
Engineering | 4 | 6% |
Linguistics | 1 | 1% |
Other | 4 | 6% |
Unknown | 12 | 17% |