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.
arXiv:2607. 04527v1 Announce Type: cross Abstract: Biological systems exhibit a hierarchical structure, characterised by directed flow from upstream regulators to downstream effects.
arXiv:2606. 24488v1 Announce Type: cross Abstract: Learning causal models from fragmented biomedical data is challenging because clinical, molecular, and imaging variables are often incomplete or not jointly observed.
arXiv:2606. 07914v1 Announce Type: cross Abstract: We study component recovery and mixing-matrix estimation from unlabeled finite mixtures whose observable distributions share the same latent components but have unknown mixing weights.
arXiv:2606. 00685v1 Announce Type: new Abstract: Gene regulatory networks (GRNs) capture transcription factor-target interactions and are central to understanding cell-state regulation and disease.
arXiv:2606. 01042v1 Announce Type: cross Abstract: Perturbation experiments are central to understanding cellular mechanisms, but remain costly and sparse, motivating prediction of gene expression responses for unobserved conditions.
arXiv:2607. 11508v1 Announce Type: cross Abstract: Causal discovery, the process of recovering underlying causal structures from observational data, is a fundamental pursuit across scientific disciplines.
arXiv:2608. 00985v1 Announce Type: new Abstract: The rapid growth of single-cell transcriptomic data has enabled the development of foundation models pretrained primarily by reconstructing masked expression values.
arXiv:2606. 30467v1 Announce Type: cross Abstract: We consider sparse multivariate stochastic systems that evolve in continuous time according to a causal mechanism and present methodology to recover the system's time-infinitesimal transition mechanism from mere cross-sectional data.
arXiv:2607. 18602v1 Announce Type: new Abstract: Cooperative gene regulation often depends on groups of regulators acting jointly, but most gene regulatory network (GRN) inference methods output pairwise regulator-target rankings.
arXiv:2607. 19866v1 Announce Type: new Abstract: Discovering the direct causes and effects of a target variable from observational data is a fundamental problem in causal discovery, with broad applications in domains such as gene regulatory analysis and biomedical research.
arXiv:2602. 04901v2 Announce Type: replace-cross Abstract: Predicting transcriptional responses to genetic perturbations is a central problem in functional genomics.
arXiv:2606. 08390v1 Announce Type: new Abstract: When a neural time-series model reports that one variable modulates another's effect on a target, is the discovered interaction a property of the data or an artifact of model flexibility?