Explaining Deep Neural Networks for the Prediction of Translation Rates
Ohler, U.; Korbel, F.; Eroshok, E.
Show abstract
A recent convolutional neural network model accurately quantifies the relationship between massively parallel synthetic 5 untranslated regions (5UTRs) and translation levels, but the underlying sequence determinants remain elusive. Applying model interpretation, we extract representations of regulatory logic, revealing a complex interplay of regulatory sequence elements. Guided by insights from model interpretation, we adapt the model by human reporter data to obtain superior performance, which will promote applications in synthetic biology and precision medicine.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Dividing out quantification uncertainty allows efficient assessment of differential transcript expression with edgeR 92%
- Dividing out quantification uncertainty enables assessment of differential transcript usage with limma and edgeR 92%
- CorrAdjust unveils biologically relevant transcriptomic correlations by efficiently eliminating hidden confounders 92%
Similar papers in this journal
- ORFeus: A Computational Method to Detect Programmed Ribosomal Frameshifts and Other Non-Canonical Translation Events 93%
- Validation of genetic variants from NGS data using Deep Convolutional Neural Networks 92%
- Comprehensive machine-learning-based analysis of microRNA-target interactions reveals variable transferability of interaction rules across species 92%
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.