Biophysical models of cis-regulation as interpretable neural networks
Tareen, A.; Kinney, J. B.
Show abstract
The adoption of deep learning techniques in genomics has been hindered by the difficulty of mechanistically interpreting the models that these techniques produce. In recent years, a variety of post-hoc attribution methods have been proposed for addressing this neural network interpretability problem in the context of gene regulation. Here we describe a complementary way of approaching this problem. Our strategy is based on the observation that two large classes of biophysical models of cis-regulatory mechanisms can be expressed as deep neural networks in which nodes and weights have explicit physiochemical interpretations. We also demonstrate how such biophysical networks can be rapidly inferred, using modern deep learning frameworks, from the data produced by certain types of massively parallel reporter assays (MPRAs). These results suggest a scalable strategy for using MPRAs to systematically characterize the biophysical basis of gene regulation in a wide range of biological contexts. They also highlight gene regulation as a promising venue for the development of scientifically interpretable approaches to deep learning.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Scalable Inference and Identifiability of Kinetic Parameters for Transcriptional Bursting from Single Cell Data 96%
- seqgra: Principled Selection of Neural Network Architectures for Genomics Prediction Tasks 95%
- Topology-Driven Negative Sampling Enhances Generalizability in Protein-Protein Interaction Prediction 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.