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You can infer gene regulatory networks from large-scale RNA-seq data by using scalable algorithms that leverage distributed computing frameworks like dask and distributed to process expression datasets efficiently.

Yes, distributed computing supports scalable inference for single-cell RNA-seq data, allowing you to accelerate network construction on multi-core and cluster environments.

Identifying transcription factor-target gene interactions in transcriptomics is best achieved through scalable network construction algorithms that process bulk and single-cell expression datasets.

Yes, you need pandas and dask to build gene regulatory networks for large datasets, as these Python libraries provide the necessary data manipulation and distributed computation capabilities for timely analysis.

The limitations of using distributed computing for gene regulatory network inference include the requirement for robust multi-core or cluster environments to handle large transcriptomics datasets efficiently.