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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">ASDS</journal-id><journal-title-group><journal-title>Applied Statistics and Data Science</journal-title></journal-title-group><issn>3066-8433</issn><eissn>3066-8441</eissn><publisher><publisher-name>Art and Design</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.61369/ASDS.2025040012</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>数据整合驱动的极大似然估计教学改革探索——以转录动力学建模为例</title><url>https://artdesignp.com/journal/ASDS/1/4/10.61369/ASDS.2025040012</url><author>杨喜艳,王子豪,吴亚豪</author><pub-date pub-type="publication-year"><year>2025</year></pub-date><volume>1</volume><issue>4</issue><history><date date-type="pub"><published-time>2025-06-20</published-time></date></history><abstract>&amp;emsp;极大似然估计（MLE）是统计推断中的核心方法，广泛应用于生命科学数据建模。随着现代生物技术的发展，实验数据呈现多样化特征，如何有效整合不同类型的数据以提高MLE准确性，已成为统计建模与生命科学交叉研究中的重要问题。本文以转录动力学为例，探讨如何通过整合nascent&amp;emsp;RNA表达数据与转录启动时间数据，精确估计随机动力学模型中的关键参数，以增强学生对数据驱动建模的理解，为相关课程的教学改革提供思路。</abstract><keywords>数据整合,极大似然估计,转录动力学建模</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>[1] Pichon X, Lagha M, Mueller F, Bertrand E. A growing toolbox to image gene expression in single cells: Sensitive approaches for demanding challenges. Mol Cell.&amp;nbsp;2018;71(3):468&amp;ndash;480.&amp;nbsp;[2] Acosta J N, Falcone G J, Rajpurkar P, et al. Multimodal biomedical AI. Nature Medicine, 2022, 28(9): 1773-1784.&amp;nbsp;[3] Nicoll A G, Szavits-Nossan J, Evans M R, et al. Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression[J]. Nature Communications, 2025, 16(1): 2833.&amp;nbsp;[4] Haberle V, Stark A. Eukaryotic core promoters and the functional basis of transcription initiation. Nat Rev Mol Cell Biol. 2018;19(10):621&amp;ndash;637.&amp;nbsp;[5] Aoi Y, Shilatifard A. Transcriptional elongation control in developmental gene expression, aging, and disease. Mol Cell. 2023;83(22):3972&amp;ndash;3999.&amp;nbsp;[6] Yang X, Wang, Z, Shi C, Zhou T, Zhang J. Deciphering HIV-1 transcription initiation and elongation from single-molecule imaging data. Research. 2025; 8: 0645.&amp;nbsp;[7] Fu X, Zhou X, Gu D, Cao Z, Grima R. DelaySSAToolkit.jl: Stochastic simulation of reaction systems with time delays in Julia. Bioinformatics. 2022;38(17):4243&amp;ndash;4245.&amp;nbsp;[8] Feldt R. BlackBoxOptim.jl. GitHub. 2019. [accessed 20 Dec 2023] https://github.com/robertfeldt/BlackBoxOptim.jl</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
