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bioRxiv · 10.1101/2021.06.22.449385

Annotating precision for integrative structural models using deep learning

Kasukurthi, N.·Viswanath, S.
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Abstract

MotivationA single precision value is currently reported for an integrative model. However, precision may vary for different regions of an integrative model owing to varying amounts of input information. ResultsWe develop PrISM (Precision for Integrative Structural Models), to efficiently identify high and low-precision regions for integrative models. AvailabilityPrISM is written in Python and available under the GNU General Public License v3.0 at https://github.com/isblab/prism; benchmark data used in this paper is available at doi:10.5281/zenodo.6241200. Contactshruthiv@ncbs.res.in Supplementary informationSupplementary data are available at Bioinformatics online.

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Kasukurthi, N., Viswanath, S.. 2021-06-22. Annotating precision for integrative structural models using deep learning. https://doi.org/10.1101/2021.06.22.449385

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