Data augmentation with generative models improves detection of Non-B DNA structures

Cherednichenko, Oleksandr; Poptsova, Maria

doi:10.1016/j.compbiomed.2024.109440

Computers in Biology and Medicine

2025

DOI: 10.1016/j.compbiomed.2024.109440

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Data augmentation with generative models improves detection of Non-B DNA structures

Oleksandr Cherednichenko,

Maria Poptsova

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Kolmogorov-Arnold Networks for Genomic Tasks

Cherednichenko,

Poptsova

2024

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Kolmogorov-Arnold Networks (KANs) emerged as a promising alternative for multilayer perceptrons in dense fully connected networks. Multiple attempts have been made to integrate KANs into various deep learning architectures in the domains of computer vision and natural language processing. Integrating KANs into deep learning models for genomic tasks has not been explored. Here, we tested linear KANs (LKANs) and convolutional KANs (CKANs) as replacement for MLP in baseline deep learning architectures for classification and generation of genomic sequences. We used three genomic benchmark datasets: Genomic Benchmarks, Genome Understanding Evaluation, and Flipon Benchmark. We demonstrated that LKANs outperformed both baseline and CKANs on almost all datasets. CKANs can achieve comparable results but struggle with scaling over large number of parameters. Ablation analysis demonstrated that the number of KAN layers correlates with the model performance. Overall, linear KANs show promising results in improving the performance of deep learning models with relatively small number of parameters. Unleashing KAN potential in different SOTA deep learning architectures currently used in genomics requires further research.

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Kolmogorov-Arnold Networks for Genomic Tasks

Cherednichenko,

Poptsova

2024

Preprint

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Data augmentation with generative models improves detection of Non-B DNA structures

Cited by 1 publication

References 31 publications

Kolmogorov-Arnold Networks for Genomic Tasks

Kolmogorov-Arnold Networks for Genomic Tasks

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