Classifying Lung Cancer Knowledge in PubMed According to GO Terms Using Extreme Learning Machine |
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| Authors: | Xia Sun Xuebin Xu Jiarong Wang Jun Feng Su‐Shing Chen |
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| Affiliation: | 1. School of Information Science and Technology, Northwest University, Xi'an, People's Republic of China;2. Systems Biology Lab, University of Florida, Gainesville, FL;3. School of Electronic and Information Engineering, Xi'an Jiatong University, Xi'an, People's Republic of China;4. Computer Information Science and Engineering, University of Florida, Gainesville, FL |
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| Abstract: | For a well‐established digital library (e.g., PubMed), searching in terms of a newly established ontology (e.g., Gene Ontology (GO)) is an extremely difficult task. Making such a digital library adaptive to any new ontology or to reorganize knowledge automatically is our main objective. The decomposition of the knowledge base into classes is a first step toward our main objective. In this paper, we will demonstrate an automated linking scheme for PubMed citations with GO terms using an improved version of extreme learning machine (ELM) type algorithms. ELM is an emergent technology, which has shown excellent performance in large data classification problems, with fast learning speeds. |
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