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- Title
Multinomial Convolutions for Joint Modeling of Regulatory Motifs and Sequence Activity Readouts.
- Authors
Park, Minjun; Singh, Salvi; Khan, Samin Rahman; Abrar, Mohammed Abid; Grisanti, Francisco; Rahman, M. Sohel; Samee, Md. Abul Hassan
- Abstract
A common goal in the convolutional neural network (CNN) modeling of genomic data is to discover specific sequence motifs. Post hoc analysis methods aid in this task but are dependent on parameters whose optimal values are unclear and applying the discovered motifs to new genomic data is not straightforward. As an alternative, we propose to learn convolutions as multinomial distributions, thus streamlining interpretable motif discovery with CNN model fitting. We developed MuSeAM (Multinomial CNNs for Sequence Activity Modeling) by implementing multinomial convolutions in a CNN model. Through benchmarking, we demonstrate the efficacy of MuSeAM in accurately modeling genomic data while fitting multinomial convolutions that recapitulate known transcription factor motifs.
- Subjects
MULTINOMIAL distribution; CONVOLUTIONAL neural networks; TRANSCRIPTION factors
- Publication
Genes, 2022, Vol 13, Issue 9, p1614
- ISSN
2073-4425
- Publication type
Article
- DOI
10.3390/genes13091614