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- W68012267 abstract "The definition of tools able to extract knowledge from structured biological data in order to support scientists research is increasing as shown by the popularity reached in the field of bioinformatics. In particular we focus our attention on the domain of assisted reproduction techniques with particular interest on the field of intracytoplasmic sperm injection. In this paper we would provide a multirelational learning framework able to discover hidden relationships between entities involved in this application domain. Our approach is based on a multirelational partitional clustering algorithm followed by a multirelational rule induction. Furthermore, the obtained rules can be represented in a easily comprehensible form and can be used as an advisor to the clinicians during their work in order to help them in determining what knowledge sources are relevant for a treatment plan." @default.
- W68012267 created "2016-06-24" @default.
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- W68012267 date "2011-01-01" @default.
- W68012267 modified "2023-09-26" @default.
- W68012267 title "A Multi-relational Learning Framework to Support Biomedical Applications" @default.
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- W68012267 doi "https://doi.org/10.1007/978-3-642-21946-7_15" @default.
- W68012267 hasPublicationYear "2011" @default.
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