{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T17:32:26Z","timestamp":1774114346341,"version":"3.50.1"},"reference-count":57,"publisher":"Oxford University Press (OUP)","issue":"Supplement_2","license":[{"start":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T00:00:00Z","timestamp":1725408000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"NIH","doi-asserted-by":"publisher","award":["1R35HG011939-01"],"award-info":[{"award-number":["1R35HG011939-01"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Measurement of single-cell gene expression at different timepoints enables the study of cell development. However, due to the resource constraints and technical challenges associated with the single-cell experiments, researchers can only profile gene expression at discrete and sparsely sampled timepoints. This missing timepoint information impedes downstream cell developmental analyses. We propose scNODE, an end-to-end deep learning model that can predict in silico single-cell gene expression at unobserved timepoints. scNODE integrates a variational autoencoder with neural ordinary differential equations to predict gene expression using a continuous and nonlinear latent space. Importantly, we incorporate a dynamic regularization term to learn a latent space that is robust against distribution shifts when predicting single-cell gene expression at unobserved timepoints. Our evaluations on three real-world scRNA-seq datasets show that scNODE achieves higher predictive performance than state-of-the-art methods. We further demonstrate that scNODE\u2019s predictions help cell trajectory inference under the missing timepoint paradigm and the learned latent space is useful for in silico perturbation analysis of relevant genes along a developmental cell path.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>The data and code are publicly available at https:\/\/github.com\/rsinghlab\/scNODE.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae393","type":"journal-article","created":{"date-parts":[[2024,6,15]],"date-time":"2024-06-15T03:01:27Z","timestamp":1718420487000},"page":"ii146-ii154","source":"Crossref","is-referenced-by-count":22,"title":["<tt>\n                      <b>scNODE<\/b>\n                    <\/tt>\n                    : generative model for temporal single cell transcriptomic data prediction"],"prefix":"10.1093","volume":"40","author":[{"given":"Jiaqi","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Computer Science, Brown University , Providence, RI 02906, United States"}]},{"given":"Erica","family":"Larschan","sequence":"additional","affiliation":[{"name":"Center for Computational Molecular Biology, Brown University , Providence, RI 02912, United States"},{"name":"Department of Molecular Biology, Cell Biology and Biochemistry, Brown University , Providence, RI 02912, United States"}]},{"given":"Jeremy","family":"Bigness","sequence":"additional","affiliation":[{"name":"Center for Computational Molecular Biology, Brown University , Providence, RI 02912, United States"}]},{"given":"Ritambhara","family":"Singh","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Brown University , Providence, RI 02906, United States"},{"name":"Center for Computational Molecular Biology, Brown University , Providence, RI 02912, United States"}]}],"member":"286","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"2024090413584396900_btae393-B1","first-page":"2623","author":"Akiba","year":"2019"},{"key":"2024090413584396900_btae393-B2","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1038\/s41587-020-0591-3","article-title":"Generalizing RNA velocity to transient cell states through dynamical modeling","volume":"38","author":"Bergen","year":"2020","journal-title":"Nat Biotechnol"},{"key":"2024090413584396900_btae393-B3","doi-asserted-by":"crossref","first-page":"e10282","DOI":"10.15252\/msb.202110282","article-title":"RNA velocity\u2013current challenges and future perspectives","volume":"17","author":"Bergen","year":"2021","journal-title":"Mol Syst Biol"},{"key":"2024090413584396900_btae393-B4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach 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