Name
Abstract | ||
|---|---|---|
Even though there are many databases for gene/protein interactions, most such data still exist only in the biomedical literature. They are spread in biomedical literature written in natural languages and they require much effort such as data mining for constructing well-structured data forms. As genomic research advances, knowledge discovery from a large collection of scientific papers is becoming more important for efficient biological and biomedical researches. In this paper, we present a relation kernel based interaction extraction method to resolve this problem. We extract gene/protein interactions of Yeast (S.cerevisiae) from text documents with relation kernel. Kernel for relation extraction is constructed with predefined interaction corpus and set of interaction patterns. Proposed relation kernel for interaction extraction only exploits shallow parsed documents. Experimental results show that the proposed kernel method achieves a recall rate of 78.3% and precision rate of 79.9% for gene/protein interaction extraction without full parsing efforts. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11552451_129 | KES (2) |
Keywords | Field | DocType |
text document,protein interaction extraction,biomedical literature,interaction extraction method,proposed relation kernel,relation kernel,interaction extraction,protein interaction,proposed kernel method,predefined interaction corpus,interaction pattern,data mining,kernel method,natural language,structured data | Kernel (linear algebra),Data mining,Computer science,Support vector machine,Tree kernel,Natural language,Artificial intelligence,Natural language processing,Knowledge extraction,Kernel method,Relationship extraction,Kernel (statistics) | Conference |
Volume | ISSN | ISBN |
3682 | 0302-9743 | 3-540-28895-3 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jae-Hong Eom | 1 | 86 | 8.91 |
| Byoung-Tak Zhang | 2 | 1571 | 158.56 |