reproducibility/references.json

[
  {
    "key": "adamson2016multiplexed",
    "title": "A Multiplexed Single-Cell CRISPR Screening Platform Enables Systematic Dissection of the Unfolded Protein Response",
    "url": "https://doi.org/10.1016/j.cell.2016.11.048",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "adduri2025predicting",
    "title": "Predicting cellular responses to perturbation across diverse contexts with State",
    "url": "https://doi.org/10.1101/2025.06.26.661135",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "ahlmann2025deep",
    "title": "Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines",
    "url": "https://doi.org/10.1038/s41592-025-02772-6",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "albergo2024stochastic",
    "title": "Stochastic interpolants with data-dependent couplings",
    "url": "https://arxiv.org/abs/2310.03725",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "boffi2025flowmap",
    "title": "Flow map matching with stochastic interpolants: A mathematical framework for consistency models",
    "url": "https://arxiv.org/abs/2406.07507",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "bunne2023cellot",
    "title": "Learning single-cell perturbation responses using neural optimal transport",
    "url": "https://doi.org/10.1038/s41592-023-01969-x",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "cellflow2025",
    "title": "CellFlow enables generative single-cell phenotype modeling with flow matching",
    "url": "https://doi.org/10.1101/2025.04.11.648220",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "chi2026departures",
    "title": "Departures: Distributional Transport for Single-Cell Perturbation Prediction with Neural Schrödinger Bridges",
    "url": "https://doi.org/10.1609/aaai.v40i25.39190",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "cui2024scgpt",
    "title": "scGPT: toward building a foundation model for single-cell multi-omics using generative AI",
    "url": "https://doi.org/10.1038/s41592-024-02201-0",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "datlinger2017pooled",
    "title": "eC-CLEM: flexible multidimensional registration software for correlative microscopies",
    "url": "https://doi.org/10.1038/nmeth.4170",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "dixit2016perturbseq",
    "title": "Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens",
    "url": "https://doi.org/10.1016/j.cell.2016.11.038",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "efron1979bootstrap",
    "title": "Bootstrap Methods: Another Look at the Jackknife",
    "url": "https://doi.org/10.1214/aos/1176344552",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "frans2025shortcut",
    "title": "One Step Diffusion via Shortcut Models",
    "url": "https://arxiv.org/abs/2410.12557",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "geng2026mean",
    "title": "Mean Flows for One-step Generative Modeling",
    "url": "https://arxiv.org/abs/2505.13447",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "gilbert2014genome",
    "title": "Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation",
    "url": "https://doi.org/10.1016/j.cell.2014.09.029",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "gonzalez2025combinatorial",
    "title": "Combinatorial prediction of therapeutic perturbations using causally inspired neural networks",
    "url": "https://doi.org/10.1038/s41551-025-01481-x",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "gretton2012kernel",
    "title": "A Kernel Two-Sample Test",
    "url": "https://jmlr.org/papers/v13/gretton12a.html",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "han2025identifying",
    "title": "Identifying an optimal perturbation to induce a desired cell state by generative deep learning",
    "url": "https://doi.org/10.1016/j.cels.2025.101405",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "hao2024large",
    "title": "Large-scale foundation model on single-cell transcriptomics",
    "url": "https://doi.org/10.1038/s41592-024-02305-7",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "jaitin2016dissecting",
    "title": "Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq",
    "url": "https://doi.org/10.1016/j.cell.2016.11.039",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "jolliffe2016pca",
    "title": "Principal component analysis: a review and recent developments",
    "url": "https://doi.org/10.1098/rsta.2015.0202",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "kamimoto2023celloracle",
    "title": "Dissecting cell identity via network inference and in silico gene perturbation",
    "url": "https://doi.org/10.1038/s41586-022-05688-9",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "kim2024consistency",
    "title": "Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion",
    "url": "https://arxiv.org/abs/2310.02279",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "lipman2023flowmatching",
    "title": "Flow Matching for Generative Modeling",
    "url": "https://arxiv.org/abs/2210.02747",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "lopez2018scvi",
    "title": "Deep generative modeling for single-cell transcriptomics",
    "url": "https://doi.org/10.1038/s41592-018-0229-2",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "loshchilov2019adamw",
    "title": "Decoupled Weight Decay Regularization",
    "url": "https://arxiv.org/abs/1711.05101",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "lotfollahi2019scgen",
    "title": "scGen predicts single-cell perturbation responses",
    "url": "https://doi.org/10.1038/s41592-019-0494-8",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "lotfollahi2023predicting",
    "title": "Predicting cellular responses to complex perturbations in high‐throughput screens",
    "url": "https://doi.org/10.15252/msb.202211517",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "norman2019exploring",
    "title": "Exploring genetic interaction manifolds constructed from rich single-cell phenotypes",
    "url": "https://doi.org/10.1126/science.aax4438",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "paszke2019pytorch",
    "title": "PyTorch: An Imperative Style, High-Performance Deep Learning Library",
    "url": "https://arxiv.org/abs/1912.01703",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "pedregosa2011sklearn",
    "title": "Scikit-learn: Machine Learning in Python",
    "url": "https://jmlr.org/papers/v12/pedregosa11a.html",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "peidli2024scperturb",
    "title": "scPerturb: harmonized single-cell perturbation data",
    "url": "https://doi.org/10.1038/s41592-023-02144-y",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "replogle2020combinatorial",
    "title": "Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing",
    "url": "https://doi.org/10.1038/s41587-020-0470-y",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "replogle2022mapping",
    "title": "Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq",
    "url": "https://doi.org/10.1016/j.cell.2022.05.013",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "roohani2024predicting",
    "title": "Predicting transcriptional outcomes of novel multigene perturbations with GEARS",
    "url": "https://doi.org/10.1038/s41587-023-01905-6",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "song2021scorebased",
    "title": "Score-Based Generative Modeling through Stochastic Differential Equations",
    "url": "https://arxiv.org/abs/2011.13456",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "song2023consistency",
    "title": "Consistency Models",
    "url": "https://proceedings.mlr.press/v202/song23a.html",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "song2024improved",
    "title": "Improved Techniques for Training Consistency Models",
    "url": "https://arxiv.org/abs/2310.14189",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "theodoris2023transfer",
    "title": "Transfer learning enables predictions in network biology",
    "url": "https://doi.org/10.1038/s41586-023-06139-9",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "wolf2018scanpy",
    "title": "SCANPY: large-scale single-cell gene expression data analysis",
    "url": "https://doi.org/10.1186/s13059-017-1382-0",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "yu2025perturbnet",
    "title": "PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations",
    "url": "https://doi.org/10.1038/s44320-025-00131-3",
    "source": "DOI registration metadata",
    "verified": "2026-09-18"
  },
  {
    "key": "zaheer2017deepsets",
    "title": "Deep Sets",
    "url": "https://arxiv.org/abs/1703.06114",
    "source": "Primary article metadata",
    "verified": "2026-09-18"
  }
]