Article

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks

2017-07-18

Abstract excerpt

<h4>Motivation</h4> Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries, or polyadenylation site. Deep learning has become the approach of choice for modeling regulatory sequences because of its strength to learn complex sequence features. However, modeling relative distances to genom...

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Literature Corpus work: 3c5d1380-3f7c-5e31-9fa9-f2b390b17bae
DOI: 10.1101/165183

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