{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:50:48Z","timestamp":1762869048170,"version":"3.41.2"},"reference-count":47,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2023,4,6]],"date-time":"2023-04-06T00:00:00Z","timestamp":1680739200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12171102"],"award-info":[{"award-number":["12171102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,5,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The dynamical properties of many complex physical and biological systems can be quantified from the energy landscape theory. Previous approaches focused on estimating the transition rate from landscape reconstruction based on data. However, for general non-equilibrium systems (such as gene regulatory systems), both the energy landscape and the probability flux are important to determine the transition rate between attractors. In this work, we proposed a data-driven approach to estimate non-equilibrium transition rate, which combines the kernel density estimation and non-equilibrium transition rate theory. Our approach shows superior performance in estimating transition rate from data, compared with previous methods, due to the introduction of a nonparametric density estimation method and the new saddle point by considering the effects of flux. We demonstrate the practical validity of our approach by applying it to a simplified cell fate decision model and a high-dimensional stem cell differentiation model. Our approach can be applied to other biological and physical systems.<\/jats:p>","DOI":"10.1093\/bib\/bbad113","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T00:08:18Z","timestamp":1681085298000},"source":"Crossref","is-referenced-by-count":4,"title":["Estimation of non-equilibrium transition rate from gene expression data"],"prefix":"10.1093","volume":"24","author":[{"given":"Feng","family":"Chen","sequence":"first","affiliation":[{"name":"Fudan University Institute of Science and Technology for Brain-Inspired Intelligence, , Shanghai 200433 , China"},{"name":"Fudan University Shanghai Center for Mathematical Sciences, , Shanghai 200433 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yubo","family":"Bai","sequence":"additional","affiliation":[{"name":"Fudan University School of Mathematical Sciences, , Shanghai 200433 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunhe","family":"Li","sequence":"additional","affiliation":[{"name":"Fudan University Institute of Science and Technology for Brain-Inspired Intelligence, , Shanghai 200433 , China"},{"name":"Fudan University Shanghai Center for Mathematical Sciences, , Shanghai 200433 , China"},{"name":"Fudan University School of Mathematical Sciences, , Shanghai 200433 , China"},{"name":"MOE Frontiers Center for Brain Science, Fudan University , Shanghai 200433 , China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2023,4,6]]},"reference":[{"key":"2023052022002707900_ref1","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1103\/RevModPhys.62.251","article-title":"Reaction-rate theory: fifty years after Kramers","volume":"62","author":"H\u00e4nggi","year":"1990","journal-title":"Rev Mod 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