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

• URL: http://arxiv.org/abs/2208.09559v1
• Date: Fri, 19 Aug 2022 22:29:30 GMT
• Title: Neural network facilitated ab initio derivation of linear formula: A case study on formulating the relationship between DNA motifs and gene expression
• Authors: Chengyu Liu, Wei Wang
• Abstract summary: We propose a framework for ab initio derivation of sequence motifs and linear formula using a new approach based on the interpretable neural network model. We showed that this linear model could predict gene expression levels using promoter sequences with a performance comparable to deep neural network models.
• Score: 8.794181445664243
• License: http://creativecommons.org/licenses/by/4.0/
• 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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