{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T20:27:58Z","timestamp":1784147278210,"version":"3.55.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2017,9,26]],"date-time":"2017-09-26T00:00:00Z","timestamp":1506384000000},"content-version":"vor","delay-in-days":1,"URL":"https:\/\/academic.oup.com\/journals\/pages\/about_us\/legal\/notices"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Molecular profiling techniques have evolved to single-cell assays, where dense molecular profiles are screened simultaneously for each cell in a population. High-throughput single-cell experiments from a heterogeneous population of cells can be experimentally and computationally sorted as a sequence of samples pseudo-temporally ordered samples. The analysis of these datasets, comprising a large number of samples, has the potential to uncover the dynamics of the underlying regulatory programmes.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We present a novel approach for modelling and inferring gene regulatory networks from high-throughput time series and pseudo-temporally sorted single-cell data. Our method is based on a first-order autoregressive moving-average model and it infers the gene regulatory network within a variational Bayesian framework. We validate our method with synthetic data and we apply it to single cell qPCR and RNA-Seq data for mouse embryonic cells and hematopoietic cells in zebra fish.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The method presented in this article is available at https:\/\/github.com\/mscastillo\/GRNVBEM.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btx605","type":"journal-article","created":{"date-parts":[[2017,9,22]],"date-time":"2017-09-22T19:12:56Z","timestamp":1506107576000},"page":"964-970","source":"Crossref","is-referenced-by-count":139,"title":["A Bayesian framework for the inference of gene regulatory networks from time and pseudo-time series data"],"prefix":"10.1093","volume":"34","author":[{"given":"M","family":"Sanchez-Castillo","sequence":"first","affiliation":[{"name":"Department of Applied Physics, University of Granada, Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D","family":"Blanco","sequence":"additional","affiliation":[{"name":"Department of Applied Physics, University of Granada, Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I M","family":"Tienda-Luna","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Granada, Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M C","family":"Carrion","sequence":"additional","affiliation":[{"name":"Department of Applied Physics, University of Granada, Granada, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufei","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, University of Texas at San Antonio, San Antonio, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2017,9,25]]},"reference":[{"key":"2023012712564853500_btx605-B1","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.cell.2014.04.005","article-title":"Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development","volume":"157","author":"Bendall","year":"2014","journal-title":"Cell"},{"key":"2023012712564853500_btx605-B2","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1093\/bioinformatics\/bts385","article-title":"A novel approach for resolving differences in single-cell gene expression patterns from zygote to blastocyst","volume":"28","author":"Buettner","year":"2012","journal-title":"Bioinformatics"},{"key":"2023012712564853500_btx605-B3","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.1002\/eji.201646347","article-title":"Computational methods for trajectory inference from single-cell transcriptomics","volume":"46","author":"Cannoodt","year":"2016","journal-title":"Eur. 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