ArticleIEEE transactions on nanobioscience2019
Predicting Local Inversions Using Rectangle Clustering and Representative Rectangle Prediction.
Article in IEEE transactions on nanobioscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Prediction and analysis of Corona Virus Disease 2019.PloS one · 2020Article
- Article
- Predicting Local Inversions Using Rectangle Clustering and Representative Rectangle Prediction.IEEE transactions on nanobioscience · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
As a specific type of structural variation, inversions are enjoying particular traction as a result of their established role in evolution. Using third-generation sequencing technology to predict inversions is growing in interest, but many such methods focus on improving sensitivity, giving rise to either too many false positives or very long running times. In this paper, we propose a new framework for inversion detection based on a combination of two novel theoretical models: rectangle clustering and representative rectangle prediction. This combination can automatically filter out false positive inversion predictions while retaining 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.
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Registered trials
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