Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery
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Abstract (arXiv)
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.
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References · 8
- 2017Attention Is All You Needcited by 60,361
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
- 2017Detecting Statistical Interactions from Neural Network Weightscited by 80
- 2018Hashing-Based-Estimators for Kernel Density in High Dimensionscited by 33
- 2020Recent Advances in Network-based Methods for Disease Gene Predictioncited by 7
- 2015Min-Max Kernelscited by 2
- 2019Towards Probabilistic Generative Models Harnessing Graph Neural Networks for Disease-Gene Predictioncited by 2
- 2019Sub-linear Memory Sketches for Near Neighbor Search on Streaming Datacited by 1
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