Innovative data augmentation strategy for deep learning on biological datasets with limited gene representations focused on chloroplast genomes

Innovative data augmentation strategy for deep learning on biological datasets with limited gene representations focused on chloroplast genomes

Abstract One key barrier to applying deep learning (DL) to omics and other biological datasets is data scarcity, particularly when each gene or protein is represented by a single sequence. This fundamental challenge is mainly relevant in research involving genetically constrained organisms, organell...

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Bibliographic Details
Main Authors: Mohammad Ali Abbasi-Vineh, Shirin Rouzbahani, Kaveh Kavousi, Masoumeh Emadpour
Format: Article
Language:English
Published: Nature Portfolio 2025-07-01
Series:Scientific Reports
Subjects:
Machine learning
Small data
Chloroplast genome
Genomics
Transcriptomics
Proteomics
Online Access:https://doi.org/10.1038/s41598-025-12796-9
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https://doi.org/10.1038/s41598-025-12796-9

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