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single-cell perturbation responses","volume":"27","author":"Song","year":"2025","journal-title":"Nat Cell Biol"},{"key":"10.1016\/j.jare.2026.06.012_b0450","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1038\/s41556-025-01622-z","article-title":"Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens","volume":"27","author":"Jiang","year":"2025","journal-title":"Nat Cell Biol"},{"key":"10.1016\/j.jare.2026.06.012_b0455","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"10.1016\/j.jare.2026.06.012_b0460","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.cell.2015.05.047","article-title":"Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with 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The conditional saddlepoint approximation for fast and accurate large-scale hypothesis testing, arXiv (2025). doi:10.48550\/arXiv.2407.08911."},{"key":"10.1016\/j.jare.2026.06.012_b0505","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1186\/s13059-014-0550-8","article-title":"Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2","volume":"15","author":"Love","year":"2014","journal-title":"Genome Biol"},{"key":"10.1016\/j.jare.2026.06.012_b0510","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1093\/bioinformatics\/btp616","article-title":"edgeR: a Bioconductor package for differential expression analysis of digital gene expression data","volume":"26","author":"Robinson","year":"2010","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jare.2026.06.012_b0515","doi-asserted-by":"crossref","first-page":"5423","DOI":"10.1038\/s41467-025-61165-7","article-title":"GPerturb: Gaussian process modelling of single-cell perturbation 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D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response, bioRxiv (2025). doi:10.1101\/2023.04.19.537364.","DOI":"10.1101\/2023.04.19.537364"},{"key":"10.1016\/j.jare.2026.06.012_b0640","article-title":"scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network","volume":"5","author":"Li","year":"2024","journal-title":"Cell Rep Med"},{"key":"10.1016\/j.jare.2026.06.012_b0645","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae466","article-title":"Single-cell and spatial multiomic inference of gene regulatory networks using SCRIPro","volume":"40","author":"Chang","year":"2024","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jare.2026.06.012_b0650","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1038\/s41592-019-0494-8","article-title":"scGen predicts single-cell perturbation responses","volume":"16","author":"Lotfollahi","year":"2019","journal-title":"Nat Methods"},{"key":"10.1016\/j.jare.2026.06.012_b0655","doi-asserted-by":"crossref","DOI":"10.15252\/msb.202211517","article-title":"Predicting cellular responses to complex perturbations in high-throughput screens","volume":"19","author":"Lotfollahi","year":"2023","journal-title":"Mol Syst Biol"},{"key":"10.1016\/j.jare.2026.06.012_b0660","doi-asserted-by":"crossref","unstructured":"\u0130necik K, Uhlmann A, Lotfollahi M, Theis FJ. MultiCPA: multimodal compositional perturbation autoencoder, bioRxiv (2022). doi:10.1101\/2022.07.08.499049.","DOI":"10.1101\/2022.07.08.499049"},{"key":"10.1016\/j.jare.2026.06.012_b0665","article-title":"scPerb: predict single-cell perturbation via style transfer-based variational autoencoder","author":"Tang","year":"2024","journal-title":"J Adv Res"},{"key":"10.1016\/j.jare.2026.06.012_b0670","doi-asserted-by":"crossref","unstructured":"Wang G, Liu T, Zhao J, Cheng Y, Zhao H. Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA, bioRxiv (2024). doi:10.1101\/2024.12.04.626878.","DOI":"10.1101\/2024.12.04.626878"},{"key":"10.1016\/j.jare.2026.06.012_b0675","article-title":"Explainable modeling of single-cell perturbation data using attention and sparse dictionary learning","volume":"16","author":"Xu","year":"2025","journal-title":"Cell Syst"},{"key":"10.1016\/j.jare.2026.06.012_b0680","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1093\/bioinformatics\/btac357","article-title":"scPreGAN, a deep generative model for predicting the response of single-cell expression to perturbation","volume":"38","author":"Wei","year":"2022","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jare.2026.06.012_b0685","doi-asserted-by":"crossref","DOI":"10.1093\/bib\/bbaf126","article-title":"CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data","volume":"26","author":"Wu","year":"2025","journal-title":"Brief Bioinform"},{"key":"10.1016\/j.jare.2026.06.012_b0690","doi-asserted-by":"crossref","first-page":"960","DOI":"10.1038\/s44320-025-00131-3","article-title":"PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations","volume":"21","author":"Yu","year":"2025","journal-title":"Mol Syst Biol"},{"key":"10.1016\/j.jare.2026.06.012_b0695","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41592-025-02877-y","article-title":"Squidiff: predicting cellular development and responses to perturbations using a diffusion model","volume":"23","author":"He","year":"2026","journal-title":"Nat Methods"},{"key":"10.1016\/j.jare.2026.06.012_b0700","doi-asserted-by":"crossref","DOI":"10.1093\/bioinformatics\/btae265","article-title":"scPRAM accurately predicts single-cell gene expression perturbation response based on attention mechanism","volume":"40","author":"Jiang","year":"2024","journal-title":"Bioinformatics"},{"key":"10.1016\/j.jare.2026.06.012_b0705","unstructured":"Demir A. sc-OTGM: single-cell perturbation modeling by solving optimal mass transport on the manifold of Gaussian mixtures, arXiv (2024). doi:10.48550\/arXiv.2405.03726."},{"key":"10.1016\/j.jare.2026.06.012_b0710","doi-asserted-by":"crossref","first-page":"773","DOI":"10.1038\/s43588-024-00698-1","article-title":"Toward subtask-decomposition-based learning and benchmarking for predicting genetic perturbation outcomes and beyond","volume":"4","author":"Gao","year":"2024","journal-title":"Nat Comput Sci"},{"key":"10.1016\/j.jare.2026.06.012_b0715","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1038\/s41586-023-06139-9","article-title":"Transfer learning enables predictions in network biology","author":"Theodoris","year":"2023","journal-title":"Nature 618"},{"key":"10.1016\/j.jare.2026.06.012_b0720","doi-asserted-by":"crossref","first-page":"1470","DOI":"10.1038\/s41592-024-02201-0","article-title":"scGPT: toward building a foundation model for single-cell multi-omics using generative AI","volume":"21","author":"Cui","year":"2024","journal-title":"Nat Methods"},{"key":"10.1016\/j.jare.2026.06.012_b0725","doi-asserted-by":"crossref","unstructured":"Chen Y, Zou J. GenePert: leveraging GenePT embeddings for gene perturbation prediction, bioRxiv (2024). doi:10.1101\/2024.10.27.620513.","DOI":"10.1101\/2024.10.27.620513"},{"key":"10.1016\/j.jare.2026.06.012_b0730","doi-asserted-by":"crossref","first-page":"101431","DOI":"10.1016\/j.patter.2025.101431","article-title":"Embeddings from language models are good learners for single-cell data analysis","volume":"7","author":"Liu","year":"2026","journal-title":"Patterns"},{"key":"10.1016\/j.jare.2026.06.012_b0735","doi-asserted-by":"crossref","first-page":"1481","DOI":"10.1038\/s41592-024-02305-7","article-title":"Large-scale foundation model on single-cell transcriptomics","volume":"21","author":"Hao","year":"2024","journal-title":"Nat Methods"},{"key":"10.1016\/j.jare.2026.06.012_b0740","doi-asserted-by":"crossref","DOI":"10.1038\/s41592-025-02822-z","article-title":"EpiAgent: foundation model for single-cell epigenomics","author":"Chen","year":"2025","journal-title":"Nat Methods"},{"key":"10.1016\/j.jare.2026.06.012_b0745","doi-asserted-by":"crossref","unstructured":"Istrate AM, Li D, Karaletsos T. scGenePT: is language all you need for modeling single-cell perturbations?, bioRxiv (2024). doi:10.1101\/2024.10.23.619972.","DOI":"10.1101\/2024.10.23.619972"},{"key":"10.1016\/j.jare.2026.06.012_b0750","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1038\/s41592-025-02980-0","article-title":"Benchmarking algorithms for generalizable single-cell perturbation response prediction","volume":"23","author":"Wei","year":"2026","journal-title":"Nat Methods"},{"key":"10.1016\/j.jare.2026.06.012_b0755","doi-asserted-by":"crossref","unstructured":"Li L, Wang S, Wang Y, Wei Z, Li P, Si D, et al. A systematic comparison of single-cell perturbation response prediction models, bioRxiv (2024). doi:10.1101\/2024.12.23.630036.","DOI":"10.1101\/2024.12.23.630036"},{"key":"10.1016\/j.jare.2026.06.012_b0760","article-title":"Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation","author":"Vi\u00f1as Torn\u00e9","year":"2025","journal-title":"Nat Biotechnol"},{"key":"10.1016\/j.jare.2026.06.012_b0765","unstructured":"Hu EJ, Shen Y, Wallis P, Allen-Zhu Z, Li Y, Wang S, et al. LoRA: low-rank adaptation of large language models, arXiv (2021). doi:10.48550\/arXiv.2106.09685."},{"key":"10.1016\/j.jare.2026.06.012_b0770","first-page":"2790","article-title":"Parameter-efficient transfer learning for NLP","volume":"97","author":"Houlsby","year":"2019","journal-title":"Proc Mach Learn Res"},{"key":"10.1016\/j.jare.2026.06.012_b0775","unstructured":"Maleki S, H\u00fctter JC, Chuang KV, Scalia G, Biancalani T. Efficient fine-tuning of single-cell foundation models enables zero-shot molecular perturbation prediction, arXiv (2024). doi:10.48550\/arXiv.2412.13478."},{"key":"10.1016\/j.jare.2026.06.012_b0780","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1038\/s42003-025-07764-y","article-title":"A large-scale benchmark for network inference from single-cell perturbation data","volume":"8","author":"Chevalley","year":"2025","journal-title":"Commun. Biol."},{"key":"10.1016\/j.jare.2026.06.012_b0785","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1186\/s12864-025-11600-2","article-title":"Benchmarking foundation cell models for post-perturbation RNA-seq prediction","volume":"26","author":"Csendes","year":"2025","journal-title":"BMC Genomics"},{"key":"10.1016\/j.jare.2026.06.012_b0790","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1186\/s13059-025-03574-x","article-title":"Zero-shot evaluation reveals limitations of single-cell foundation models","volume":"26","author":"Kedzierska","year":"2025","journal-title":"Genome Biol"},{"key":"10.1016\/j.jare.2026.06.012_b0795","doi-asserted-by":"crossref","first-page":"69391","DOI":"10.52202\/075280-3039","article-title":"xTrimoGene: an efficient and scalable representation learner for single-cell RNA-seq data","volume":"36","author":"Gong","year":"2023","journal-title":"Adv. Neural Inf. Process. 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