KAN-Mixer: Kolmogorov-Arnold Networks for Gene Expression Prediction in Plant Species
Abstract
Understanding the intricate relationships between cis-regulatory elements and gene expression is crucial for decoding genetic regulation in ecological systems. In this study, we introduce a novel application of Kolmogorov-Arnold Networks (KANs) for predicting gene expression across diverse plant species, including Arabidopsis thaliana, Solanum lycopersicum, Sorghum bicolor, and Zea mays. Our model, named KAN-Mixer, utilizes k-mers as inductive biases to capture biologically relevant patterns in nucleotide sequences. By employing token embeddings and mixer architectures, KAN-Mixer enhances both the interpretability and usability of KANs. Our results indicate that KAN-Mixer achieves comparable accuracy to ConvNet-based approaches while offering superior interpretability, making it a robust tool for ecological data analysis in a variety of environments.
Cite
Text
Gao et al. "KAN-Mixer: Kolmogorov-Arnold Networks for Gene Expression Prediction in Plant Species." European Conference on Computer Vision Workshops, 2024. doi:10.1007/978-3-031-92387-6_9Markdown
[Gao et al. "KAN-Mixer: Kolmogorov-Arnold Networks for Gene Expression Prediction in Plant Species." European Conference on Computer Vision Workshops, 2024.](https://mlanthology.org/eccvw/2024/gao2024eccvw-kanmixer/) doi:10.1007/978-3-031-92387-6_9BibTeX
@inproceedings{gao2024eccvw-kanmixer,
title = {{KAN-Mixer: Kolmogorov-Arnold Networks for Gene Expression Prediction in Plant Species}},
author = {Gao, Jin and Zhao, Juntu and Li, Keyu and Wang, Dequan},
booktitle = {European Conference on Computer Vision Workshops},
year = {2024},
pages = {135-150},
doi = {10.1007/978-3-031-92387-6_9},
url = {https://mlanthology.org/eccvw/2024/gao2024eccvw-kanmixer/}
}