Predicting emergent phenotypes from single cell populations using CELLECTION
Hu, H.; Sanghi, S.; Quon, G.
Show abstract
Biological systems exhibit emergent phenotypes that arise from the collective behavior of individual components, such as whole-organ functions that arise from the coordinated activity of its individual cells, or organism-level phenotypes that result from the functional interplay of collections of genes in the genome. We present CELLECTION, a deep learning framework that learns to associate subgroups of instances with different emergent phenotypes. We show CELLECTION enables interpretable predictions for heterogeneous tasks, including disease classification, identification of disease-associated cell subtypes, alignment of developmental stages between human model systems, and even predicting relative hand-wing indices across the avian lineage. CELLECTION therefore provides a scalable and flexible framework for identifying key cellular or genetic signatures underlying complex traits in development, disease, and evolution.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Cell type-specific gene expression dynamics during human brain maturation 96%
- Dynamic network-guided CRISPRi screen reveals CTCF loop-constrained nonlinear enhancer-gene regulatory activity in cell state transitions 96%
- Atlas of genetic effects in human microglia transcriptome across brain regions, aging and disease pathologies 96%
Similar papers in this journal
- Network-aware self-supervised learning enables high-content phenotypic screening for genetic modifiers of neuronal activity dynamics 96%
- Self-iterative multiple instance learning enables the prediction of CD4+ T cell immunogenic epitopes 95%
- INSCT: Integrating millions of single cells using batch-aware triplet neural networks 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.