Neural network facilitated ab initio derivation of linear formula: A case study on formulating the relationship between DNA motifs and gene expression. (arXiv:2208.09559v1 [q-bio.QM])

Developing models with high interpretability and even deriving formulas to
quantify relationships between biological data is an emerging need. We propose
here a framework for ab initio derivation of sequence motifs and linear formula
using a new approach based on the interpretable neural network model called
contextual regression model. We showed that this linear model could predict
gene expression levels using promoter sequences with a performance comparable
to deep neural network models. We uncovered a list of 300 motifs with …

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