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Code for Effective gene expression prediction from sequence by integrating long-range interactions

Authors: Avsec, Žiga; Agarwal, Vikram; Visentin, Daniel; Ledsam, Joseph R.; Grabska-Barwinska‎, Agnieszka; Taylor, Kyle R.; Assael, Yannis; +3 Authors

Code for Effective gene expression prediction from sequence by integrating long-range interactions

Abstract

This package provides an implementation of the Enformer model and examples on running the model. If this source code or accompanying files are helpful for your research please cite the following publication: "Effective gene expression prediction from sequence by integrating long-range interactions" Žiga Avsec, Vikram Agarwal, Daniel Visentin, Joseph R. Ledsam, Agnieszka Grabska-Barwinska, Kyle R. Taylor, Yannis Assael, John Jumper, Pushmeet Kohli, David R. Kelley Please see also https://github.com/deepmind/deepmind-research/tree/master/enformer.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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