Sara Aibar, Carmen Bravo González-Blas, Thomas Moerman, Vân Anh Huynh- Thu, Hana Imrichova, Gert Hulselmans, Florian Rambow, Jean-Christophe Marine, Pierre Geurts, Jan Aerts, et al. 2017. SCENIC: Single-cell regulatory network inference and clustering. Nature Methods 14, 11 (2017), 1083-1086.
Michael S Albergo, Michael Lindsey, Nicholas M Boffi, and Eric Vanden-Eijnden. 2024. Multimarginal generative modeling with stochastic interpolants. In 12th International Conference on Learning Representations, ICLR 2024.
Pierre-Cyril Aubin-Frankowski and Jean-Philippe Vert. 2020. Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference. Bioinformatics 36, 18 (2020), 4774-4780.
Pau Badia-i Mompel, Lorna Wessels, Sophia Müller-Dott, Rémi Trimbour, Ricardo O Ramirez Flores, Ricard Argelaguet, and Julio Saez-Rodriguez. 2023. Gene regulatory network inference in the era of single-cell multi-omics. Nature Reviews Genetics 24, 11 (2023), 739-754.
Thalia E Chan, Michael PH Stumpf, and Ann C Babtie. 2017. Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures. Cell Systems 5, 3 (2017), 251-267.
Jiaxing Chen, ChinWang Cheong, Liang Lan, Xin Zhou, Jiming Liu, Aiping Lyu, William K Cheung, and Lu Zhang. 2021. DeepDRIM: a deep neural network to reconstruct cell-type-specific gene regulatory network using single-cell RNA-seq data. Briefings in Bioinformatics 22, 6 (2021), bbab325.
Shuonan Chen and Jessica C Mar. 2018. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinformatics 19, 1 (2018), 232.
Gökcen Eraslan, Lukas M Simon, Maria Mircea, Nikola S Mueller, and Fabian J Theis. 2019. Single-cell RNA-seq denoising using a deep count autoencoder. Nature Communications 10, 1 (2019), 390.
Jeremiah J Faith, Boris Hayete, Joshua T Thaden, Ilaria Mogno, Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J Collins, and Timothy S Gardner. 2007. Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles. PLOS Biology 5, 1 (2007), e8.
Yue Fan and Xiuli Ma. 2021. Gene regulatory network inference using 3D convolutional neural network. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 35. AAAI Press, 99-106.
Jonas Simon Fleck, Sophie Martina Johanna Jansen, Damian Wollny, Fides Zenk, Makiko Seimiya, Akanksha Jain, Ryoko Okamoto, Malgorzata Santel, Zhisong He, J Gray Camp, et al. 2023. Inferring and perturbing cell fate regulomes in human brain organoids. Nature 621, 7978 (2023), 365-372.
Luz Garcia-Alonso, Christian H. Holland, Mahmoud M. Ibrahim, Denes Turei, and Julio Saez-Rodriguez. 2019. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Research 29, 8 (2019), 1363-1375.
Anne-Claire Haury, Fantine Mordelet, Paola Vera-Licona, and Jean-Philippe Vert. 2012. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Systems Biology 6, 1 (2012), 145.
Mo Huang, Jingshu Wang, Eduardo Torre, Hannah Dueck, Sydney Shaffer, Roberto Bonasio, John I Murray, Arjun Raj, Mingyao Li, and Nancy R Zhang. 2018. SAVER: gene expression recovery for single-cell RNA sequencing. Nature Methods 15, 7 (2018), 539-542.
Vân Anh Huynh-Thu and Pierre Geurts. 2018. dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data. Scientific Reports 8, 1 (2018), 3384.
Vân Anh Huynh-Thu, Alexandre Irrthum, Louis Wehenkel, and Pierre Geurts. 2010. Inferring regulatory networks from expression data using tree-based methods. PLOS ONE 5, 9 (2010), e12776.
Ruochen Jiang, Tianyi Sun, Dongyuan Song, and Jingyi Jessica Li. 2022. Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biology 23, 1 (2022), 31.
Kenji Kamimoto, Blerta Stringa, Christy M Hoffmann, Kunal Jindal, Lilianna Solnica-Krezel, and Samantha A Morris. 2023. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 7949 (2023), 742-751.
Philipp Keyl, Philip Bischoff, Gabriel Dernbach, Michael Bockmayr, Rebecca Fritz, David Horst, Nils Blüthgen, Grégoire Montavon, Klaus-Robert Müller, and Frederick Klauschen. 2023. Single-cell gene regulatory network prediction by explainable AI. Nucleic Acids Research 51, 4 (2023), e20.
Seongho Kim. 2015. ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients. Communications for Statistical Applications and Methods 22, 6 (2015), 665-674.
Jens Uwe Loers and Vanessa Vermeirssen. 2024. A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data. Briefings in Bioinformatics 25, 5 (2024), bbae382.
Lam-Ha Ly and Martin Vingron. 2022. Effect of imputation on gene network reconstruction from single-cell RNA-seq data. Patterns 3, 2 (2022), 11 pages.
Adam A Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins, Gustavo Stolovitzky, Riccardo Dalla Favera, and Andrea Califano. 2006. ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinformatics 7, 1 (2006), S7.
Hirotaka Matsumoto, Hisanori Kiryu, Chikara Furusawa, Minoru SH Ko, Shigeru BH Ko, Norio Gouda, Tetsutaro Hayashi, and Itoshi Nikaido. 2017. SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation. Bioinformatics 33, 15 (2017), 2314-2321.
Leland McInnes, John Healy, and James Melville. 2018. UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 doi:10. 48550/arXiv.1802.03426
Thomas Moerman, Sara Aibar Santos, Carmen Bravo González-Blas, Jaak Simm, Yves Moreau, Jan Aerts, and Stein Aerts. 2019. GRNBoost2 and Arboreto: efficient and scalable inference of gene regulatory networks. Bioinformatics 35, 12 (2019), 2159-2161.
Kevin R Moon, David Van Dijk, Zheng Wang, Scott Gigante, Daniel B Burkhardt, William S Chen, Kristina Yim, Antonia van den Elzen, Matthew J Hirn, Ronald R Coifman, et al. 2019. Visualizing structure and transitions in high-dimensional biological data. Nature Biotechnology 37, 12 (2019), 1482-1492.
Sophia Müller-Dott, Eirini Tsirvouli, Miguel Vazquez, Ricardo O Ramirez Flores, Pau Badia-i Mompel, Robin Fallegger, Dénes Türei, Astrid Lægreid, and Julio Saez-Rodriguez. 2023. Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. Nucleic Acids Research 51, 20 (2023), 10934-10949.
Nan Papili Gao, SM Minhaz Ud-Dean, Olivier Gandrillon, and Rudiyanto Gunawan. 2018. SINCERITIES: inferring gene regulatory networks from timestamped single cell transcriptional expression profiles. Bioinformatics 34, 2 (2018), 258-266.
Michael Poli, Stefano Massaroli, Atsushi Yamashita, Hajime Asama, Jinkyoo Park, and Stefano Ermon. 2021. TorchDyn: implicit models and neural numerical methods in PyTorch. In Neural Information Processing Systems, Workshop on Physical Reasoning and Inductive Biases for the Real World, Vol. 2.
Aditya Pratapa, Amogh P Jalihal, Jeffrey N Law, Aditya Bharadwaj, and TM Murali. 2020. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature Methods 17, 2 (2020), 147-154.
Xiaojie Qiu, Arman Rahimzamani, Li Wang, Bingcheng Ren, Qi Mao, Timothy Durham, José L McFaline-Figueroa, Lauren Saunders, Cole Trapnell, and Sreeram Kannan. 2020. Inferring Causal Gene Regulatory Networks from Coupled Single- Cell Expression Dynamics Using Scribe. Cell Systems 10, 3 (2020), 265-274.
Caleb C Reagor, Nicolas Velez-Angel, and A J Hudspeth. 2023. Depicting pseudotime-lagged causality across single-cell trajectories for accurate generegulatory inference. PNAS Nexus 2, 4 (2023), pgad113.
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, and Yvan Saeys. 2019. A comparison of single-cell trajectory inference methods. Nature Biotechnology 37, 5 (2019), 547-554.
Manuel Sanchez-Castillo, David Blanco, Isabel M Tienda-Luna, MC Carrion, and Yufei Huang. 2018. A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data. Bioinformatics 34, 6 (2018), 964-970.
Manu Setty, Michelle D Tadmor, Shlomit Reich-Zeliger, Omer Angel, Tomer Meir Salame, Pooja Kathail, Kristy Choi, Sean Bendall, Nir Friedman, and Dana Pe'er. 2016. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nature biotechnology 34, 6 (2016), 637-645.
Yutong Sha, Yuchi Qiu, Peijie Zhou, and Qing Nie. 2024. Reconstructing growth and dynamic trajectories from single-cell transcriptomics data. Nature Machine Intelligence 6, 1 (2024), 25-39.
Hantao Shu, Jingtian Zhou, Qiuyu Lian, Han Li, Dan Zhao, Jianyang Zeng, and Jianzhu Ma. 2021. Modeling gene regulatory networks using neural network architectures. Nature Computational Science 1, 7 (2021), 491-501.
David M Simcha, Laurent Younes, Martin J Aryee, and Donald Geman. 2013. Identification of direction in gene networks from expression and methylation. BMC systems biology 7 (2013), 1-15.
Alicia T Specht and Jun Li. 2017. LEAP: constructing gene co-expression networks for single-cell RNA-sequencing data using pseudotime ordering. Bioinformatics 33, 5 (2017), 764-766.
Kelly Street, Davide Risso, Russell B Fletcher, Diya Das, John Ngai, Nir Yosef, Elizabeth Purdom, and Sandrine Dudoit. 2018. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19 (2018), 1-16.
Alexander Tong, Jessie Huang, Guy Wolf, David Van Dijk, and Smita Krishnaswamy. 2020. TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics. In 37th International Conference on Machine Learning. PMLR, 9526-9536.
Alexander Tong, Nikolay Malkin, Kilian Fatras, Lazar Atanackovic, Yanlei Zhang, Guillaume Huguet, Guy Wolf, and Yoshua Bengio. 2024. Simulation-free Schrödinger bridges via score and flow matching. In 27th International Conference on Artificial Intelligence and Statistics. 1279-1287.
Koen Van den Berge, Hector Roux de Bézieux, Kelly Street, Wouter Saelens, Robrecht Cannoodt, Yvan Saeys, Sandrine Dudoit, and Lieven Clement. 2020. Trajectory-based differential expression analysis for single-cell sequencing data. Nature communications 11, 1 (2020), 1201.
Monique GP Van Der Wijst, Dylan H de Vries, Harm Brugge, Harm-Jan Westra, and Lude Franke. 2018. An integrative approach for building personalized gene regulatory networks for precision medicine. Genome Medicine 10 (2018), 1-15.
David Van Dijk, Roshan Sharma, Juozas Nainys, Kristina Yim, Pooja Kathail, Ambrose J Carr, Cassandra Burdziak, Kevin R Moon, Christine L Chaffer, Diwakar Pattabiraman, et al. 2018. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 3 (2018), 716-729.
Florian Wagner, Yun Yan, and Itai Yanai. 2017. K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data. BioRxiv (2017), 217737.
AoranWang and Jun Pang. 2022. Iterative structural inference of directed graphs. In 36th International Conference on Neural Information Processing Systems. Curran Associates, Inc., 8717-8730.
Aoran Wang, Tsz Pan Tong, Andrzej Mizera, and Jun Pang. 2024. Benchmarking Structural Inference Methods for Interacting Dynamical Systems with Synthetic Data. In Advances in Neural Information Processing Systems, Vol. 37. Curran Associates, Inc., 135129-135185.
AoranWang, Tsz Pan Tong, and Jun Pang. 2023. Effective and Efficient Structural Inference with Reservoir Computing. In 40th International Conference on Machine Learning. PMLR, 36391-36410.
StevenWoodhouse, Nir Piterman, Christoph M Wintersteiger, Berthold Göttgens, and Jasmin Fisher. 2018. SCNS: a graphical tool for reconstructing executable regulatory networks from single-cell genomic data. BMC Systems Biology 12 (2018), 1-7.
