| Autor: |
Shanfeng Zhu, Yasushi Okuno, Gozoh Tsujimoto, Hiroshi Mamitsuka |
| Jazyk: |
angličtina |
| Rok vydání: |
2006 |
| Předmět: |
|
| Zdroj: |
Cancer Informatics, Vol 2, Pp 361-371 (2006) |
| Druh dokumentu: |
article |
| ISSN: |
1176-9351 |
| Popis: |
An important issue in current medical science research is to find the genes that are strongly related to an inherited disease. A particular focus is placed on cancer-gene relations, since some types of cancers are inherited. As bio-medical databases have grown speedily in recent years, an informatics approach to predict such relations from currently available databases should be developed. Our objective is to find implicit associated cancer-genes from biomedical databases including the literature database. Co-occurrence of biological entities has been shown to be a popular and efficient technique in biomedical text mining. We have applied a new probabilistic model, called mixture aspect model (MAM) [48], to combine different types of co-occurrences of genes and cancer derived from Medline and OMIM (Online Mendelian Inheritance in Man). We trained the probability parameters of MAM using a learning method based on an EM (Expectation and Maximization) algorithm. We examined the performance of MAM by predicting associated cancer gene pairs. Through cross-validation, prediction accuracy was shown to be improved by adding gene-gene co-occurrences from Medline to cancer-gene cooccurrences in OMIM. Further experiments showed that MAM found new cancer-gene relations which are unknown in the literature. Supplementary information can be found at http://www.bic.kyotou.ac.jp/pathway/zhusf/CancerInformatics/Supplemental2006.html |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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