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Developing models with high interpretability and even deriving formulas toquantify relationships between biological data is an emerging need. We proposehere a framework for ab initio derivation of sequence motifs and linear formulausing a new approach based on the interpretable neural network model calledcontextual regression model. We showed that this linear model could predictgene expression levels using promoter sequences with a performance comparableto deep neural network models. We uncovered a list of 300 motifs with importantregulatory roles on gene expression and showed that they also had significantcontributions to cell-type specific gene expression in 154 diverse cell types.This work illustrates the possibility of deriving formulas to represent biologylaws that may not be easily elucidated.(https://github.com/Wang-lab-UCSD/Motif_Finding_Contextual_Regression)

Neural network facilitated ab initio derivation of linear formula: A  case study on formulating the relationship between DNA motifs and gene  expression