Single-cell modeling

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

No dataset record in the archive lists this task.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Most implemented papers archive 2025-07-28

3 shown of 3 papers with code (4 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• The Deep Generative Decoder: MAP estimation of representations improves modeling of single-cell RNA data
  13 Oct 2021 2 repositories listed Syntology ran 0 of 7 samples · 7 unverified
  Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis.
• Foundation Models Meet Imbalanced Single-Cell Data When Learning Cell Type Annotations
  27 Oct 2023 1 repository listed
  We benchmark three foundation models, scGPT, scBERT, and Geneformer, using skewed single-cell cell-type distribution for cell-type annotation.
• SISUA: Semi-Supervised Generative Autoencoder for Single Cell Data
  8 May 2019 1 repository listed
  In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a…

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.