The study investigates how phenotypic changes can be encoded as low‑dimensional modulations of a self‑organizing developmental system, using neural cellular automata (NCAs) as a model. By applying low‑rank adaptation (LoRA) to pretrained NCAs, the authors show that simple rank‑one adjustments can control horizontal and vertical scaling of a 2D emoji phenotype, and that these adaptations generalize across diverse phenotypes sharing the same regulatory scaffold. Analysis of thousands of phenotype‑specific NCA adapters reveals latent low‑dimensional directions that govern scaling, style, and symmetrical fission, offering a computational realization of D’Arcy Thompson’s grid transformations in a minimal cybernetic tissue.
arXiv:2605. 14998v3 Announce Type: replace Abstract: From subcellular structures to entire organisms, many natural systems generate complex organisation through self-organisation: local interactions that collectively give rise to global structure without any blueprint of the outcome.
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:2506. 11152v4 Announce Type: replace-cross Abstract: Single-cell transcriptomics and proteomics have become a great source for data-driven insights into biology, enabling the use of advanced deep learning methods to understand cellular heterogeneity and gene expression at the single-cell level.
arXiv:2602. 15253v2 Announce Type: replace Abstract: Neural scaling laws -- power-law relationships between loss, model size, and data -- have been extensively documented for language and vision transformers, yet their existence in single-cell genomics remains largely unexplored.
arXiv:2506. 22228v2 Announce Type: replace-cross Abstract: Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions.
The paper investigates a cellular‑automata substrate where each pixel hosts a tiny neural network that senses its neighbors and survives only by self‑replication with mutated weights. Starting from a few founders, the system evolves into a spatially organized ecosystem of competing species, and the authors introduce coarse‑grained metrics to quantify phenotypic and genotypic diversity at two scales. Experiments show that high phenotypic diversity reduces genotypic diversity and vice versa, and that full‑genome hash colouring reveals lineage structures missed by random‑weight probes.
arXiv:2608. 02961v1 Announce Type: cross Abstract: We study a self-supervised generation task for single-cell gene expression vectors: given a set of vectors from a cell type, we aim to generate additional gene expression vectors of that cell type.
arXiv:2608. 15306v1 Announce Type: cross Abstract: High-throughput single-cell and spatial transcriptomic technologies provide high-resolution snapshots of heterogeneous cellular states, but their destructive nature prevents repeated measurements of the same cells over time.
arXiv:2607. 23258v1 Announce Type: new Abstract: Large-scale calcium imaging has created an opportunity to build foundation-style models for neural population dynamics, but a central question remains unresolved: \textbf{whether a model pretrained on one collection of recordings can generalize to new datasets, experimental paradigms, and even species.
arXiv:2608. 14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H\&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST).
arXiv:2607. 14410v1 Announce Type: new Abstract: Spatially resolved omics studies increasingly combine transcriptomic and epigenomic assays, yet downstream analysis is often still performed using single-modality pipelines.
arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.