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• W2912998328 abstract "As a specific type of structural variation, inversions are particularly interesting due to their established role in evolution. Using third-generation sequencing technology for predicting inversions is growing in interest, but many such methods focus on improving sensitivity, resulting in either too many false positives or very long runtime. In this paper, we propose a new framework for inversion detection based on the combination of two novel theoretical models: Rectangle Clustering and Representative Rectangle Prediction. This combination can automatically filter out false positive inversion predictions while retaining the correct ones, leading to a method that has both high sensitivity and high positive prediction values (PPV). Further, this new framework can run very fast on available data. Our software can be freely obtained at https://github.com/UTbioinf/RigInv." @default.
• W2912998328 created "2019-02-21" @default.
• W2912998328 creator A5019857186 @default.
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• W2912998328 date "2018-12-01" @default.
• W2912998328 modified "2023-10-18" @default.
• W2912998328 title "Predicting Local Inversions Using Rectangle Clustering and Representative Rectangle Prediction" @default.
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• W2912998328 doi "https://doi.org/10.1109/bibm.2018.8621190" @default.
• W2912998328 hasPublicationYear "2018" @default.
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