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- W2238460068 abstract "Aims: To predict human proteins that could belong to Reactome pathways. To compare different Representations of proteomic knowledge and Relational Learning techniques. To take advantages from the interactions and other important biological associations. To face up to multi-class and multi-label classification, frequent in functional annotations problems. We propose Machine learning strategies to solve functional annotation tasks." @default.
- W2238460068 created "2016-06-24" @default.
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- W2238460068 date "2013-09-04" @default.
- W2238460068 modified "2023-09-28" @default.
- W2238460068 title "The relevance of systems biology in relational learning to annotate human protein with Reactome pathways" @default.
- W2238460068 doi "https://doi.org/10.7490/f1000research.1094191.1" @default.
- W2238460068 hasPublicationYear "2013" @default.
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