  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 important
regulatory roles on gene expression and showed that they also had significant
contributions to cell-type specific gene expression in 154 diverse cell types.
This work illustrates the possibility of deriving formulas to represent biology
laws that may not be easily elucidated.
(https://github.com/Wang-lab-UCSD/Motif_Finding_Contextual_Regression)
