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• W4375954757 abstract "The number of biological databases is growing rapidly, but different databases use different identifiers (IDs) to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed MantaID, a data-driven, machine learning-based approach that automates identifying IDs on a large scale. The MantaID model's prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within 2 min. MantaID supports the discovery and exploitation of ID from large quantities of databases (e.g. up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application and application programming interfaces were also developed for MantaID to improve applicability. To our knowledge, MantaID is the first tool that enables an automatic, quick, accurate and comprehensive identification of large quantities of IDs and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases." @default.
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• W4375954757 date "2023-01-01" @default.
• W4375954757 modified "2023-09-28" @default.
• W4375954757 title "MantaID: a machine learning–based tool to automate the identification of biological database IDs" @default.
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• W4375954757 doi "https://doi.org/10.1093/database/baad028" @default.
• W4375954757 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37159241" @default.
• W4375954757 hasPublicationYear "2023" @default.
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