Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery
This incremental improvement addresses data efficiency for researchers in computational biology analyzing gene-gene interactions.
The paper tackled the bottleneck of parameter-intensive Transformer models in gene-gene interaction discovery by introducing a weighted diversified sampling algorithm, achieving comparable performance with only 1% of a single-cell dataset.
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.