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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.15616 |
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| _version_ | 1866912079288467456 |
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| author | Wu, Yifan Yang, Yuntao Liu, Zirui Li, Zhao Pahwa, Khushbu Li, Rongbin Zheng, Wenjin Hu, Xia Xu, Zhaozhuo |
| author_facet | Wu, Yifan Yang, Yuntao Liu, Zirui Li, Zhao Pahwa, Khushbu Li, Rongbin Zheng, Wenjin Hu, Xia Xu, Zhaozhuo |
| contents | Gene-gene interactions play a crucial role in the manifestation of complex human diseases. Uncovering significant gene-gene interactions is a challenging task. Here, we present an innovative approach utilizing data-driven computational tools, leveraging an advanced Transformer model, to unearth noteworthy gene-gene interactions. Despite the efficacy of Transformer models, their parameter intensity presents a bottleneck in data ingestion, hindering data efficiency. To mitigate this, we introduce a novel weighted diversified sampling algorithm. This algorithm computes the diversity score of each data sample in just two passes of the dataset, facilitating efficient subset generation for interaction discovery. Our extensive experimentation demonstrates that by sampling a mere 1\% of the single-cell dataset, we achieve performance comparable to that of utilizing the entire dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_15616 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery Wu, Yifan Yang, Yuntao Liu, Zirui Li, Zhao Pahwa, Khushbu Li, Rongbin Zheng, Wenjin Hu, Xia Xu, Zhaozhuo Artificial Intelligence Gene-gene interactions play a crucial role in the manifestation of complex human diseases. Uncovering significant gene-gene interactions is a challenging task. Here, we present an innovative approach utilizing data-driven computational tools, leveraging an advanced Transformer model, to unearth noteworthy gene-gene interactions. Despite the efficacy of Transformer models, their parameter intensity presents a bottleneck in data ingestion, hindering data efficiency. To mitigate this, we introduce a novel weighted diversified sampling algorithm. This algorithm computes the diversity score of each data sample in just two passes of the dataset, facilitating efficient subset generation for interaction discovery. Our extensive experimentation demonstrates that by sampling a mere 1\% of the single-cell dataset, we achieve performance comparable to that of utilizing the entire dataset. |
| title | Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2410.15616 |