arXiv Machine Learning June 2, 2026 By Gongxu Luo, Boyang Sun, Kun Zhang

On the Recoverability of Causal Relations from Bulk Gene Expression Data

Read the original on arXiv Machine Learning →

arXiv:2606. 00568v1 Announce Type: new Abstract: Bulk gene expression profiling, which aggregates pooled RNA across cells within a biological sample, remains important in the single-cell era because it is typically less noisy, more sensitive, and more cost-effective than single-cell assays.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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Causal ASCEND: Scalable Two-tier Causal Discovery on High Dimensional Multi-omics Data

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