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

Liu, C.; Wang, W.

doi:10.1101/2022.08.19.504467

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bioRxiv · 10.1101/2022.08.19.504467

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

Liu, C.·Wang, W.

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Abstract

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)

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Liu, C., Wang, W.. 2022-08-22. Neural network facilitated ab initio derivation of linear formula: A case study on formulating the relationship between DNA motifs and gene expression. https://doi.org/10.1101/2022.08.19.504467

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