Summary of An Embarrassingly Simple Approach to Enhance Transformer Performance in Genomic Selection For Crop Breeding, by Renqi Chen et al.
An Embarrassingly Simple Approach to Enhance Transformer Performance in Genomic Selection for Crop Breeding
by Renqi Chen, Wenwei Han, Haohao Zhang, Haoyang Su, Zhefan Wang, Xiaolei Liu, Hao Jiang, Wanli Ouyang, Nanqing Dong
First submitted to arxiv on: 15 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel Transformer-based framework is proposed for genomic selection (GS) to overcome the limitations of statistical methods. By leveraging attention mechanisms and simple preprocessing techniques like k-mer tokenization and random masking, the framework achieves superior performance on rice3k and wheat3k datasets compared to seminal methods. The work demonstrates the potential of deep learning in capturing non-linear relationships between markers for crop breeding. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Genomic selection helps produce more food to address hunger globally. Currently, statistical methods are used, but they have limitations. A new approach uses deep learning, like Transformers, to find patterns in DNA sequences. However, these models can be tricky because of the limited data available. In this study, a simple yet effective Transformer-based framework is proposed that can learn from long DNA sequences. The results show that this approach performs better than other methods on rice and wheat datasets. |
Keywords
» Artificial intelligence » Attention » Deep learning » Tokenization » Transformer